Prediction of Solid Waste Generation Rates in Urban Region of Laos Using Socio-Demographic and Economic Parameters with a Multi Linear Regression Approach
Abstract
:1. Introduction
2. Methods and Data Collection
2.1. Overview of Study Area and Definition of Solid Waste in Laos
2.2. Multi-Linear Regression
2.3. Variable Selection
2.4. Data Collection
2.4.1. Data Collection for the Independent Variables
2.4.2. Data Collection for the Dependent Variable
3. Results
3.1. Model Assumptions
3.2. Model Accuracy and Validity
3.3. SWGR Prediction from 1995 to 2050
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Ferronato, N.; Torretta, V. Waste mismanagement in developing countries: A review of global issues. Int. J. Environ. Res. Public Health 2019, 16, 1060. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Alessandra, G.; Dicma, B.; Garfí, M. Waste Disposal in Developing Countries and Emergency Situations. The Case of Saharawi Refugees Camps. Available online: https://www.iswa.org/uploads/tx_iswaknowledgebase/15-384paper_long.pdf (accessed on 8 February 2021).
- MONRE. National Pollution Control Strategy and Action Plan. 2017. Available online: http://www.gms-eoc.org/uploads/resources/922/attachment/Laos-Pollution-Strategy-Plan-2018-2025-draft.pdf (accessed on 18 November 2020).
- Chen, H.W.; Chang, N. Bin Prediction analysis of solid waste generation based on grey fuzzy dynamic modeling. Resour. Conserv. Recycl. 2000, 29, 1–18. [Google Scholar] [CrossRef]
- Popli, K.; Luckman Sudibya, G.; Kim, S. A Review of Solid Waste Management using System Dynamics Modeling. J. Environ. Sci. Int. 2017, 1185–1200. [Google Scholar] [CrossRef]
- Abbasi, M.; El Hanandeh, A. Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manag. 2016, 56, 13–22. [Google Scholar] [CrossRef] [PubMed]
- Intharathirat, R.; Abdul Salam, P.; Kumar, S.; Untong, A. Forecasting of municipal solid waste quantity in a developing country using multivariate grey models. Waste Manag. 2015, 39, 3–14. [Google Scholar] [CrossRef]
- Al-Salem, S.M.; Al-Nasser, A.; Al-Dhafeeri, A.T. Multi-variable regression analysis for the solid waste generation in the State of Kuwait. Process Saf. Environ. Prot. 2018, 119, 172–180. [Google Scholar] [CrossRef]
- Kolekar, K.A.; Hazra, T.; Chakrabarty, S.N. A review on prediction of municipal solid waste generation models. Procedia Environ. Sci. 2016, 35, 238–244. [Google Scholar] [CrossRef]
- Lakioti, E.N.; Moustakas, K.G.; Komilis, D.P.; Domopoulou, A.E.; Karayannis, V.G. Sustainable solid waste management: Socio-economic considerations. Chem. Eng. Trans. 2017, 56, 661–666. [Google Scholar] [CrossRef]
- Suthar, S.; Singh, P. Household solid waste generation and composition in different family size and socio-economic groups: A case study. Sustain. Cities Soc. 2015, 14, 56–63. [Google Scholar] [CrossRef]
- Vieira, V.H.A.d.M.; Matheus, D.R. The impact of socioeconomic factors on municipal solid waste generation in São Paulo, Brazil. Waste Manag. Res. 2018, 36, 79–85. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Trang, P.T.T.; Dong, H.Q.; Toan, D.Q.; Hanh, N.T.X.; Thu, N.T. The Effects of Socio-economic Factors on Household Solid Waste Generation and Composition: A Case Study in Thu Dau Mot, Vietnam. Energy Procedia 2017, 107, 253–258. [Google Scholar] [CrossRef]
- Hidalgo, J.; Amaya, J.; Jervis, F.; Moreira, C. Influence of Socio-Economic Factors on Household Solid Waste (HSW) Generation of the City of Guayaquil, Ecuador. In Proceedings of the Sixteen Laccei International Multi-Conference for Engineering, Education and Technology, Montego Bay, Jamaica, 24–26 July 2019. [Google Scholar] [CrossRef] [Green Version]
- Buenrostro, O.; Bocco, G.; Vence, J. Forecasting generation of urban solid waste in developing countries—A case study in Mexico. J. Air Waste Manag. Assoc. 2001, 51, 86–93. [Google Scholar] [CrossRef] [Green Version]
- Wei, Y.; Xue, Y.; Yin, J.; Ni, W. Prediction of municipal solid waste generation in china by multiple linear regression method. Int. J. Comput. Appl. 2013, 35, 136–140. [Google Scholar] [CrossRef]
- Kumar, A.; Samadder, S.R. An empirical model for prediction of household solid waste generation rate—A case study of Dhanbad, India. Waste Manag. 2017, 68, 3–15. [Google Scholar] [CrossRef]
- Asian Development Bank. Lao People’s Democratic Republic: Urban Development Sector Assessment, Strategy, and Road Map; Asian Development Bank: Mandaluyong, Philippines, 2012; ISBN 978-92-9092-872-0. [Google Scholar]
- Assembly, N. Lao PDR Environmental Protection Law. 2012. Available online: http://extwprlegs1.fao.org/docs/pdf/lao151747.pdf (accessed on 18 November 2020).
- NRB Chapter 8: Multiple Linear Regression Natural Resources Biometrics. Available online: https://milnepublishing.geneseo.edu/natural-resources-biometrics/chapter/chapter-8-multiple-linear-regression/ (accessed on 18 November 2020).
- Popli, K.; Lim, J.; Kim, H.K.; Kim, Y.M.; Tuu, N.T.; Kim, S. Prediction of greenhouse gas emission from municipal solid waste for South Korea. Environ. Eng. Res. 2020, 25, 462–469. [Google Scholar] [CrossRef]
- Sivakumar, K.; Sugirtharan, M. Impact of Family Income and Size on Per Capita Solid Waste. J. Sci. Univ. Kelaniya 2010, 5, 13–23. [Google Scholar] [CrossRef] [Green Version]
- Monavari, S.M.; Omrani, G.A.; Karbassi, A.; Raof, F.F. The effects of socioeconomic parameters on household solid-waste generation and composition in developing countries (a case study: Ahvaz, Iran). Environ. Monit. Assess. 2012, 184, 1841–1846. [Google Scholar] [CrossRef] [PubMed]
- The World Bank Group. Drivers of Poverty Reduction in Lao PDR. Available online: https://openknowledge.worldbank.org/handle/10986/2119 (accessed on 18 November 2020).
- Ali, N.E.; Sion, H.C. Solid waste management in Asian countries: A review of solid waste minimisation (3′r) towards low carbon. IOP Conf. Ser. Earth Environ. Sci. 2014, 18. [Google Scholar] [CrossRef] [Green Version]
- Liu, J.; Li, Q.; Gu, W.; Wang, C. The impact of consumption patterns on the generation of municipal solid waste in China: Evidences from provincial data. Int. J. Environ. Res. Public Health 2019, 16, 1717. [Google Scholar] [CrossRef] [Green Version]
- World Bank World Bank Open Data Data. Available online: https://data.worldbank.org/ (accessed on 9 February 2021).
- Centre, N.S.; SCP Laos of Lao PDR. Atlas of Laos. 1995. Available online: http://www.diva-portal.org/smash/get/diva2:818132/FULLTEXT01.pdf (accessed on 19 November 2020).
- Laos Census. Results of Lao PDR. 2005. Available online: https://data.opendevelopmentmekong.net (accessed on 19 November 2020).
- Lao PDR. The 4th Population and Housing Census (PHC). 2015. Available online: https://lao.unfpa.org/sites/default/files/pub-pdf/PHC-ENG-FNAL-WEB_0.pdf (accessed on 19 November 2020).
- Warr, P.; Rasphone, S.; Menon, J. Two Decades of Declining Poverty Despite Rising Inequality in Laos. 2015. Available online: https://www.adb.org/sites/default/files/publication/176031/ewp-461.pdf (accessed on 19 November 2020).
- Kiralj, R.; Ferreira, M.M.C. Basic validation procedures for regression models in QSAR and QSPR studies: Theory and application. J. Braz. Chem. Soc. 2009, 20, 770–787. [Google Scholar] [CrossRef] [Green Version]
- JICA Present Solid Waste Management in Vientiane 1992. Available online: https://openjicareport.jica.go.jp/pdf/11008042_02.pdf (accessed on 19 November 2020).
- Khanal, B.R.; Souksavath, B. Environmental Management Measures and Current Practices in Solid Waste Management: A Case Study from Vientiane, Lao People’s Democratic Republic. J. GMS Dev. Stud. 2005, 2, 69–89. [Google Scholar]
- Glawe, U.; Visvanathan, C.; Alamgir, M. Solid Waste Management in Least Developed Asian Countries—A Comparative Analysis. In Proceedings of the International Conference on Integrated Solid Waste Management in Southeast Asian Cities: Proceedings, Siem Reap, Cambodia, 5–7 July 2005; pp. 1–11. [Google Scholar]
- Shekdar, A.V. Sustainable solid waste management: An integrated approach for Asian countries. Waste Manag. 2009, 29, 1438–1448. [Google Scholar] [CrossRef] [PubMed]
- UNEP. MSW Management Report. 2010. Available online: http://www.rrcap.ait.ac.th/Publications/MW_status_quo.pdf (accessed on 20 November 2020).
- IGES. A Guide for Improving Municipal Solid Waste Management and Promoting Urban Organic Waste Utilization in Lao PDR. 2012. Available online: https://www.iges.or.jp/en/pub/guide-improving-municipal-solid-waste/en (accessed on 6 March 2020).
- MPWT; MONRE. Fourth Regional 3R Forum in Asia Brief Country Analysis Paper Lao PDR. 2013, pp. 18–20. Available online: https://www.uncrd.or.jp (accessed on 20 November 2020).
- CCAC Solid Waste Management City Profile—Vientiane Capital. LAO People’s Democratic Republic, 2015.
- STEA. Lao People’s Democratic Republic The First National Communication on Climate Change. 2000. Available online: https://unfccc.int/resource/docs/natc/laonc1.pdf (accessed on 23 November 2020).
- Garth, A. Analysing data using SPSS (A practical guide for those unfortunate enough to have to actually do it). 2008. Available online: https://students.shu.ac.uk/lits/it/documents/pdf/analysing_data_using_spss.pdf (accessed on 20 November 2020).
- Mishra, P.; Pandey, C.M.; Singh, U.; Gupta, A.; Sahu, C.; Keshri, A. Descriptive statistics and normality tests r statistical data. Ann. Card. Anaesth. 2019, 22, 67–72. [Google Scholar] [CrossRef]
- Ferre, J. Regression Diagnostics. In Contemporary Sociology; Sage: Thousand Oaks, CA, USA, 2009; Volume 21, p. 548. [Google Scholar]
- Lewis-Beck, M.; Bryman, A.; Futing Liao, T. Stepwise Regression. In The SAGE Encyclopedia of Social Science Research Methods; SAGE Publications, Inc.: Thousand Oaks, CA, USA, 2012; pp. 1–9. [Google Scholar]
- Ratner, B. The correlation coefficient: Its values range between 1/1, or do they. J. Target. Meas. Anal. Mark. 2009, 17, 139–142. [Google Scholar] [CrossRef] [Green Version]
- Li, Z.; Yao, J. Testing for heteroscedasticity in high-dimensional regressions. Econom. Stat. 2019, 9, 122–139. [Google Scholar] [CrossRef]
- MONRE. MONRE Vision Toward 2030. 2015. Available online: http://extwprlegs1.fao.org/docs/pdf/lao170969.pdf (accessed on 20 November 2020).
- MONRE. National Biodiversity Strategy and Action Plan 2016—2025. 2016. Available online: https://leap.unep.org/countries/la/national-legislation/national-biodiversity-strategy-and-action-plan-2016-2025 (accessed on 20 November 2020).
Type | Variable Name | Type | Description |
---|---|---|---|
Dependent Variable (DV) | Urban Solid Waste Generation Rates (SWGR) | Ton per year (TPY) | It is defined as the rate of generation of solid waste in the urban region. |
Independent Variable (IV) | Gross Domestic Product (GDP) per capita | Current United States Dollars (USD) | It is defined as gross domestic product divided by midyear population. Gross domestic product is the sum of gross value added by all resident producers in the economy plus any product taxes and minus any subsidies not included in the value of the products. |
Independent Variable | Urban Population (Population/Pop) | People | It is defined as the population living in the urban areas as defined by the national statistical offices of respective countries. |
Independent Variable | Urban Literacy Rate (Literacy Rate/LR) | Percent (%) | It is defined as the rate of urban population to have the ability to identify, understand, interpret, create, communicate and compute, use printed and written materials associated with varying contexts. Here population aged 15 years and above is considered with literacy rates in the urban region of Laos. |
Independent Variable | Urban Poverty Incidence (Poverty Incidence/PI) | Percent (%) | It is defined as the percentage of the urban population living below the national poverty lines (Source: World Bank). National estimates are based on population-weighted subgroup estimates from household surveys (Source: Census of Laos). |
Independent Variable | Urban Household Size (Household Size/HS) | People Per Household (PPH) | It is defined as a household/social unit consisting of one or more persons who live together under the same roof and make common arrangements in the provision of food and other living conditions for themselves irrespective of blood relation or marriage in urban areas. |
Independent Variable | Urban Unemployment Rate (Unemployment Rate/UR) | Percent (%) | It is defined as the rate at which the urban population is without work, currently available for work, seeking work in the recent past period, people who lost their jobs and includes people who voluntarily left their job. |
Type | Dependent Variable | Independent Variables | |||||
---|---|---|---|---|---|---|---|
Year | SWGR (TPY) | GDP per capita (USD) | Population (People) | Literacy Rate (%) | Poverty Incidence (%) | Household Size (PPH) | Unemployment Rate (%) |
1995 | 161,391 | 364 | 842,222 | 76.60 | 23.86 | 6.00 | 4.50 |
1996 | 181,237 | 378 | 902,801 | 77.87 | 22.98 | 5.94 | 4.46 |
1997 | 202,659 | 346 | 965,620 | 79.14 | 22.10 | 5.88 | 4.42 |
1998 | 225,773 | 249 | 1,030,927 | 80.41 | 21.62 | 5.82 | 4.38 |
1999 | 250,698 | 278 | 1,098,951 | 81.68 | 21.14 | 5.76 | 4.34 |
2000 | 277,580 | 325 | 1,169,990 | 82.95 | 20.66 | 5.70 | 4.30 |
2001 | 296,748 | 327 | 1,243,825 | 84.22 | 20.18 | 5.64 | 4.26 |
2002 | 316,865 | 320 | 1,320,796 | 85.49 | 19.70 | 5.58 | 4.22 |
2003 | 338,011 | 363 | 1,401,187 | 86.76 | 19.24 | 5.52 | 4.18 |
2004 | 360,413 | 418 | 1,485,877 | 88.03 | 18.78 | 5.46 | 4.14 |
2005 | 381,353 | 476 | 1,563,651 | 89.30 | 18.32 | 5.40 | 4.10 |
2006 | 397,778 | 591 | 1,622,169 | 89.72 | 17.86 | 5.35 | 4.06 |
2007 | 415,020 | 710 | 1,683,371 | 90.14 | 17.40 | 5.30 | 4.02 |
2008 | 433,038 | 900 | 1,747,050 | 90.56 | 15.92 | 5.25 | 3.98 |
2009 | 451,619 | 949 | 1,812,307 | 90.98 | 14.44 | 5.20 | 3.94 |
2010 | 470,669 | 1141 | 1,878,749 | 91.40 | 12.96 | 5.15 | 3.90 |
2011 | 490,141 | 1378 | 1,946,164 | 91.82 | 11.48 | 5.10 | 3.86 |
2012 | 510,106 | 1581 | 2,014,818 | 92.24 | 10.00 | 5.05 | 3.82 |
2013 | 530,602 | 1826 | 2,084,844 | 92.66 | 8.52 | 5.00 | 3.78 |
2014 | 551,844 | 1998 | 2,157,058 | 93.08 | 7.04 | 4.95 | 3.74 |
2015 | 573,944 | 2135 | 2,231,865 | 93.50 | 5.56 | 4.90 | 3.70 |
Hypothesis Test Summary: One-Sample Kolmogorov-Smirnov Test | ||||||||
---|---|---|---|---|---|---|---|---|
Type of Variable/Statistics | DV | IV | TV | |||||
SWGR | GDP | Pop | LR | PI | HS | UR | InvGDP | |
Mean | 372,261.38 | 812.05 | 1,533,535.33 | 87.07 | 16.66 | 5.43 | 4.10 | 0.00 |
Standard Deviation | 126,122.758 | 619.813 | 434,560.633 | 5.407 | 5.440 | 0.342 | 0.248 | 0.001 |
Significant number | 0.200 1,2 | 0.05 1 | 0.200 1,2 | 0.063 1 | 0.0991 | 0.200 1,2 | 0.200 1,2 | 0.200 1,2 |
Decision for the null hypothesis | Retain | Reject | Retain | Retain | Retain | Retain | Retain | Retain |
Type of Variable/Statistics | DV | IV | TV | |||||
---|---|---|---|---|---|---|---|---|
SWGR | GDP | Pop | LR | PI | HS | UR | InvGDP | |
Skewness Statistics (SS) | −0.101 | 1.055 | −0.033 | −0.641 | −0.727 | 0.127 | 0.000 | 0.085 |
Standard Error-Skewness (SES) | 0.501 | 0.501 | 0.501 | 0.501 | 0.501 | 0.501 | 0.501 | 0.0501 |
ZS | −0.201 | 2.105 | −0.066 | −1.280 | −1.450 | 0.253 | 0.000 | 0.170 |
Kurtosis Statistics (KS) | −1.115 | −0.251 | −1.229 | −0.946 | −0.600 | −1.191 | −1.200 | −1.424 |
Standard Error Kurtosis (SEK) | 0.972 | 0.972 | 0.972 | 0.972 | 0.972 | 0.972 | 0.972 | 0.972 |
ZK | −1.147 | −0.258 | −1.264 | −0.973 | −0.617 | −1.225 | −1.235 | −1.465 |
Scenario | Model | Included IV | Unstandardized Beta Coefficients | p-Value | VIF | Tolerance | Comments |
---|---|---|---|---|---|---|---|
Scenario 1 | Constant | −1,116,300.616 | 0.000 | ||||
InvGDP | −27,956,844.271 | 0.003 | 3.759 | 0.266 | |||
Literacy Rate | 17,728.688 | 0.000 | 3.759 | 0.266 | |||
Scenario 2 | Model 1 | Constant | 2,374,879.277 | 0.000 | |||
Household Size | −369,065.167 | 0.000 | 1.000 | 1.000 | |||
Model 2 | Constant | 2,303,206.560 | 0.000 | Presence of multi-collinearity | |||
Household Size | −352,509.475 | 0.000 | 10.980 | 0.091 | |||
Poverty Incidence | −1090.445 | 0.012 | 10.980 | 0.091 | |||
Model 3 | Constant | 1,169,994.593 | 0.002 | Presence of multi-collinearity | |||
Household Size | −219,017.073 | 0.000 | 652.847 | 0.002 | |||
Poverty Incidence | −4433.539 | 0.000 | 113.071 | 0.009 | |||
Literacy Rate | 5334.977 | 0.002 | 256.856 | 0.004 | |||
Scenario 3 | Constant | 709,656.153 | 0.000 | ||||
InvGDP | −28,081,498.413 | 0.079 | 4.812 | 0.208 | |||
Poverty Incidence | −16,932.000 | 0.000 | 4.812 | 0.208 | |||
Scenario 4 | Model 1 | Constant | −72,478.318 | 0.000 | |||
Population | 0.290 | 0.000 | 1.000 | 1.000 | |||
Model 2 | Constant | −120,245.001 | 0.000 | ||||
Population | 0.310 | 0.000 | 6.344 | 0.158 | |||
InvGDP | 8,406,640.193 | 0.000 | 6.344 | 0.158 | |||
Dependent variable: SWGR |
InvGDP | Pop | PI | HS | UR | SWGR | ||
---|---|---|---|---|---|---|---|
InvGDP | Pearson Correlation | 1 | −0.918 ** | 0.890 ** | 0.909 ** | 0.916 ** | −0.905 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
Pop | Pearson Correlation | −0.918 ** | 1 | −0.961 ** | −0.999 ** | −1.000 ** | 0.999 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
PI | Pearson Correlation | 0.890 ** | −0.961 ** | 1 | 0.953 ** | 0.965 ** | −0.957 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
HS | Pearson Correlation | 0.909 ** | −0.999 ** | 0.953 ** | 1 | 0.999 ** | −1.000 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
UR | Pearson Correlation | 0.916 ** | −1.000 ** | 0.965 ** | 0.999 ** | 1 | −0.999 ** |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | ||
SWGR | Pearson Correlation | −0.905 ** | 0.999 ** | −0.957 ** | −1.000 ** | −0.999 ** | 1 |
Sig. (2-tailed) | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Scenarios | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | ||||
---|---|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | ||||
Predictors (DV: SWGR) | InvGDP, LR | HS | HS, PI | HS, PI, LR | InvGDP, PI | Pop | Pop, InvGDP | |
Model Summary | R square (R2) | 0.973 | 0.999 | 1.000 | 1.000 | 0.930 | 0.998 | 0.999 |
Adjusted R square | 0.970 | 0.999 | 0.999 | 1.000 | 0.922 | 0.998 | 0.999 | |
Standard Error of Estimate | 21,899.392 | 3356.025 | 2885.686 | 2237.220 | 35,171.722 | 5049.880 | 3268.159 | |
Durbin Watson Factor | 0.278 | 1.022 | 0.154 | 1.179 | ||||
ANOVA | F | 322.683 | 28,227.611 | 19,093.433355 | 21,181.759745 | 119.588 | 12,456.408 | 14,883.936 |
Sig. | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
Scenario | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | |||
---|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | |||
Predictor variables | InvGDP, LR | HS | HS, PI | HS, PI, LR | InvGDP, PI | Pop | Pop, InvGDP |
p-value | 0.179 | 0.128 | 0.544 | 0.291 | 0.006 | 0.001 | 0.062 |
Result (Homoscedasticity (Pass)/Heteroscedasticity (Fail)) | Pass | Pass | Pass | Pass | Fail | Fail | Pass |
PVI | Scenario 1 | Scenario 2 | Scenario 3 | Scenario 4 | |||
---|---|---|---|---|---|---|---|
Model 1 | Model 2 | Model 3 | Model 1 | Model 2 | |||
InvGDP, LR | HS | HS, PI | HS, PI, LR | InvGDP, PI | Pop | ||
MAE | 16,557.18 | 2575.75 | 2293.16 | 1552.92 | 26,523.45 | 3475.04 | 1886.40 |
RMSE | 20,278.68 | 3192.27 | 2671.62 | 2012.86 | 32,571.25 | 4803.40 | 3030.74 |
MAPE | 4.49 | 0.71 | 0.72 | 0.53 | 9.35 | 1.36 | 0.67 |
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Popli, K.; Park, C.; Han, S.-M.; Kim, S. Prediction of Solid Waste Generation Rates in Urban Region of Laos Using Socio-Demographic and Economic Parameters with a Multi Linear Regression Approach. Sustainability 2021, 13, 3038. https://doi.org/10.3390/su13063038
Popli K, Park C, Han S-M, Kim S. Prediction of Solid Waste Generation Rates in Urban Region of Laos Using Socio-Demographic and Economic Parameters with a Multi Linear Regression Approach. Sustainability. 2021; 13(6):3038. https://doi.org/10.3390/su13063038
Chicago/Turabian StylePopli, Kanchan, Chunkyoo Park, Sang-Min Han, and 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 13, no. 6: 3038. https://doi.org/10.3390/su13063038