Forecasting municipal solid waste generation using prognostic tools and regression analysis

Ghinea, Cristina; Drăgoi, Elena Niculina; Comăniţă, Elena-Diana; Gavrilescu, Marius; Campean, Teofil; Curteanu, Silvia; Gavrilescu, Maria

doi:10.1016/j.jenvman.2016.07.026

Cited by 149 publications

(75 citation statements)

References 29 publications

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“…In the process of waste management planning, a perpetual reliable data is necessary when predicting the waste generation as mentioned in Ghinea et al (2016). In the forecasting method, counts are frequently based on the demographic and socioeconomic factors on a per capita basis.…”

Section: Methodsmentioning

confidence: 99%

Multilinear regression analysis on solid waste generation quantity in Malaysia towards sustainable development

Zulkipli

2017

Int. j. adv. appl. sci.

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Inadequate data will affect the efficiency of future planning of solid waste management in order to achieve sustainable development. The purpose of this paper is to investigate the effect of a number of factors, namely GDP, Demand of electricity, Population and Number of Employment, which could be applied to predict the solid waste generation quantities and improve the management of future planning. The data were statistically analyzed by conducting a bivariate analysis and multilinear regression analysis. The results revealed that the GDP, Demand of electricity, Population and Number of Employment reflects the prediction of sustainable solid waste generation. It was found that addition of all predictor variables accounted for 98.9 percent (r = 0.989) changes in the variance in the quantity of solid waste generation. Consequently, the department of solid waste can increase its effectiveness and efficiency in management through the prediction of the quantity of solid waste generation.

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Section: Methodsmentioning

confidence: 99%

Multilinear regression analysis on solid waste generation quantity in Malaysia towards sustainable development

Zulkipli

2017

Int. j. adv. appl. sci.

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“…The quantities of MSW change over time. The quantities of MSW have been growing rapidly in many nations or regions [34][35][36][37]. In order to better reflect the changing trend of MSW collection quantities, 10 years of historical data in MSW collection from 2008 to 2017 for 287 cities have been used, and we can hypothesize the following: Hypothesis 2.…”

Section: The Design Of the Time Variable And Hypothesismentioning

confidence: 99%

“…Multivariate grey models, artificial intelligence models, S-curve trend models, and hidden Markov models, etc. have been used by researchers for forecasting MSW quantities [32][33][34][35][36][37][38]. The majority of previous studies mainly focus on MSW collection trends and quantity predictions.…”

Section: Introductionmentioning

confidence: 99%

Regional Differences in Municipal Solid Waste Collection Quantities in China

Zhou

Chu

et al. 2019

Sustainability

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The rapid growth in urban population has led to a dramatic increase in municipal solid waste (MSW) generation, with ramifications more pronounced in developing countries. The regional Chinese governments have made great efforts to reduce MSW generation and collection quantities. However, the results of these efforts vary across cities. The purpose of this paper is to analyze the regional differences in MSW collection quantities. A two-level hierarchical linear model (HLM) was used to examine the variations in MSW collection quantities among 287 prefecture-level cites in China over the period from 2008 to 2017. The analysis reveals a strong negative correlation between the regional economic development level and the growth trend of MSW collection quantities. The empirical findings indicate that the level of economic development and waste collection measures are critical determinants of MSW collection quantities.

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“…However, the complex link between consumers behaviors and MWMS mass flows remains a significant and sensitive element of these assessment studies. In order to address this issues, [14] have successfully developed a mathematical model to forecast municipal solid waste generation based on prognostic tools and regression analysis. Although such models are well suited to assess MWMS in stable conditions, they can hardly capture social impacts such as incentives, media campaigns or social interactions between households.…”

Section: Solid Waste Managementmentioning

confidence: 99%

Agent-Based Model for End-of-Life Product Flow Analysis

Labelle

Frayret

2018

Resources

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This paper presents an agent-based simulation model for end-of-life product flow analysis in recuperation and recycling supply networks that focuses on individual consumer behaviors. The simulation model is applied to a deposit-return program on wine bottles that could be developed in the province of Quebec. Canadian data was used to calibrate and validate the model. A series of experiments was then conducted with three artificial populations to analyse how they would react to several implementation scenarios of this end-of-life product flow strategy. The results suggest that the distance to the nearest depot is an important decision factor, but less predominant than the ownership of a private vehicle and the deposit value. The results also indicate that the use of agent-based modeling combined with the theory of planned behavior (TPB) can produce modular behavior models, that are intuitive and simple, to better understand consumer-behavior-driven supply chains. Such models can be used to give insights to decision-makers and policy-makers about the potential performance of end-of-life product flows strategies and further facilitate efficient resource management.

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Forecasting municipal solid waste generation using prognostic tools and regression analysis

Cited by 149 publications

References 29 publications

Multilinear regression analysis on solid waste generation quantity in Malaysia towards sustainable development

Multilinear regression analysis on solid waste generation quantity in Malaysia towards sustainable development

Regional Differences in Municipal Solid Waste Collection Quantities in China

Agent-Based Model for End-of-Life Product Flow Analysis

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