scholarly journals Does China’s Municipal Solid Waste Source Separation Program Work? Evidence from the Spatial-Two-Stage-Least Squares Models

2020 ◽  
Vol 12 (4) ◽  
pp. 1664 ◽  
Author(s):  
Liange Zhao ◽  
Jianfeng Zou ◽  
Zhijian Zhang
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Spatial Interaction ◽  
Source Separation ◽  
Spillover Effect ◽  
Absolute Amount ◽  
Waste Generation ◽  
Solid Waste Generation ◽  
The Absolute ◽  
The Impact

This paper evaluates the impact of the second municipal solid waste (MSW) source separation program on municipal solid waste generation (MSWG) in China. Without considering the spatial interactions between cities, the second MSW source separation program has a nonsignificant adverse effect on the per capita municipal solid waste generation (PMSWG). Relaxing the stable-unit-treatment-value assumption (SUTVA), which holds in most of the previous estimation literature on treatment effects, involving the spatial spillover effect among cities, as well as correcting the endogenous local policy has a significantly negative but not robust impact on the PMSWG. The estimation results of the generalized nesting spatial regression models (GNS) imply that the spatial interaction characteristics among Chinese prefecture-level cities may, if neglected, lead to underestimation of the reduction effects of the second MSW source separation policy on the absolute amount of PMSWG. More importantly, our study indicates that although not all the spatial econometric models support the significant reduction effect of source separation on the absolute amount of PMSWG, the source separation program significantly reduces the relative amount of PMSWG, and this result is robust in all spatial models.

The impact of tourism on municipal solid waste generation: The case of Menorca Island (Spain)

2013 ◽  
Vol 33 (12) ◽  
pp. 2589-2593 ◽  
Author(s):  
Josep Mateu-Sbert ◽  
Ignacio Ricci-Cabello ◽  
Ester Villalonga-Olives ◽  
Elena Cabeza-Irigoyen
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Waste Generation ◽  
Solid Waste Generation ◽  
The Impact

scholarly journals Application of Modified Grey Forecasting Model to Predict the Municipal Solid Waste Generation using MLP and MLE

2021 ◽  
Vol 6 (5) ◽  
pp. 1276-1296
Author(s):  
Mohd Anjum ◽  
Sana Shahab ◽  
Mohammad Sarosh Umar
Keyword(s):  
Linear Regression ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Likelihood Estimation ◽  
Grey Model ◽  
Waste Generation ◽  
Elementary Model ◽  
Grey Forecasting ◽  
Solid Waste Generation ◽  
Forecasting Theory

Grey forecasting theory is an approach to build a prediction model with limited data to produce better forecasting results. This forecasting theory has an elementary model, represented as the GM(1,1) model , characterized by the first-order differential equation of one variable. It has the potential for accurate and reliable forecasting without any statistical assumption. The research proposes a methodology to derive the modified GM(1,1) model with improved forecasting precision. The residual series is forecasted by the GM(1,1) model to modify the actual forecasted values. The study primarily addresses two fundamental issues: sign prediction of forecasted residual and the procedure for formulating the grey model. Accurate sign prediction is very complex, especially when the model lacks in data. The signs of forecasted residuals are determined using a multilayer perceptron to overcome this drawback. Generally, the elementary model is formulated conventionally, containing the parameters that cannot be calculated straightforward. Therefore, maximum likelihood estimation is incorporated in the modified model to resolve this drawback. Three statistical indicators, relative residual, posterior variance test, and absolute degree of grey indices, are evaluated to determine the model fitness and validation. Finally, an empirical study is performed using actual municipal solid waste generation data in Saudi Arabia, and forecasting accuracies are compared with the linear regression and original GM(1,1). The MAPEs of all models are rigorously examined and compared, and then it is obtained that the forecasting precision of GM(1,1) model , modified GM(1,1) model, and linear regression is 15.97%, 8.90%, and 27.90%, respectively. The experimental outcomes substantiate that the modified grey model is a more suitable forecasting approach than the other compared models.

Municipal solid waste generation in China: influencing factor analysis and multi-model forecasting

2018 ◽  
Vol 20 (3) ◽  
pp. 1761-1770 ◽  
Author(s):  
Leaksmy Chhay ◽  
Md Amjad Hossain Reyad ◽  
Rathny Suy ◽  
Md Rafiqul Islam ◽  
Md Manik Mian
Keyword(s):  
Factor Analysis ◽  
Municipal Solid Waste ◽  
Solid Waste ◽  
Influencing Factor ◽  
Waste Generation ◽  
Solid Waste Generation
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