Comparative study of municipal solid waste disposal in three Chinese representative cities

Duan, Ning; Li, Dan; Wang, Ping; Ma, Wenchao; Wenga, Terrence; Zhong, Lei; Chen, Guanyi

doi:10.1016/j.jclepro.2020.120134

Cited by 59 publications

(24 citation statements)

References 36 publications

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“…Forecasting the behavior of waste generation has become a fundamental part of developing and designing different management plans for a city. To be able to forecast this behavior, different tools have been explored, including statistical analysis [25], comparative analysis to identify the representative variables involved in the generation process [26] and techniques such as the computational approach based on the k-means algorithm and self-organizing map (SOM) [27]. With acceptable confidence levels, all these techniques are appropriate for this type of analysis, to generate useful and accurate information so that municipal administrations can make better decisions when developing management plans.…”

Section: Discussionmentioning

confidence: 99%

Analytical Methodology for the Identification of Critical Zones on the Generation of Solid Waste in Large Urban Areas

Meza

Rodrigo-Ilarri

Hernandez

et al. 2020

IJERPH

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One of the main environmental issues to address in large urban areas is the ever-increasing generation of municipal solid waste (MSW) and the need to manage it properly. Despite significant efforts having been made to implement comprehensive solid waste management systems, current management methods often do not provide sustainable alternatives which ensure the reduction of solid waste generation. This paper presents an analytical methodology that employs a combination of geographic information system techniques (GIS) along with statistical and numerical optimization methods to evaluate solid waste generation in large urban areas. The methodology was successfully applied to evaluate MSW generation in different exclusive service areas (ASES) of the city of Bogotá (Colombia). The results of the analysis on the solid waste generation data in each collection area in terms of its socioeconomic level are presented below. These socioeconomic levels are explained by defining different strata in terms of their purchasing power. The results demonstrate the usefulness of these GIS and numerical optimization techniques as a valuable complementary tool to analyze and design efficient and sustainable solid waste management systems.

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Section: Discussionmentioning

confidence: 99%

Analytical Methodology for the Identification of Critical Zones on the Generation of Solid Waste in Large Urban Areas

Meza

Rodrigo-Ilarri

Hernandez

et al. 2020

IJERPH

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show abstract

“…The selection of influencing factors has an important impact on the results and rationality of modeling. In [38], the characteristics, influencing factors and components of MSW were compared and analyzed. Results showed that the main indicators of influencing factors to MSW were all of economic development levels, population and investments of government input.…”

Section: Selection Of Influencing Factorsmentioning

confidence: 99%

“…Results showed that the main indicators of influencing factors to MSW were all of economic development levels, population and investments of government input. Based on the results of [38] and the characteristics of urban development in Wuhan, this paper takes five factors as input factors, namely, resident population at the end of the year, road sweeping area, passenger accommodation and passenger volume, per capita net income and resident consumption index. See Table 2 for the original data on the input variables.…”

Section: Selection Of Influencing Factorsmentioning

confidence: 99%

Fractional derivative multivariable grey model for nonstationary sequence and its application

Kang

Mao

Zhang

et al. 2020

J. of Syst. Eng. Electron.

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Most of the existing multivariable grey models are based on the 1-order derivative and 1-order accumulation, which makes the parameters unable to be adjusted according to the data characteristics of the actual problems. The results about fractional derivative multivariable grey models are very few at present. In this paper, a multivariable Caputo fractional derivative grey model with convolution integral CFGMC(q, N) is proposed. First, the Caputo fractional difference is used to discretize the model, and the least square method is used to solve the parameters. The orders of accumulations and differential equations are determined by using particle swarm optimization (PSO). Then, the analytical solution of the model is obtained by using the Laplace transform, and the convergence and divergence of series in analytical solutions are also discussed. Finally, the CFGMC(q, N) model is used to predict the municipal solid waste (MSW). Compared with other competition models, the model has the best prediction effect. This study enriches the model form of the multivariable grey model, expands the scope of application, and provides a new idea for the development of fractional derivative grey model.

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“…The challenge in data collection is a common issue in both developing and developed countries although data collection systems have progressed for the latter. It is reflected in studies focusing on developing models for waste prediction or forecasting, including China (Duan et al 2020), Vietnam (Nguyen et al 2021), Thailand (Sun and Chungpaibulpatana 2017), India (Kumar and Samadder 2017), South Africa (Ayeleru et al 2021), Canada (Kannangara et al 2018), the Czech Republic (Pavlas et al 2020), Brazil (Teixeira et al 2020), the Russian Federation (Gil'mundinov, Tagaeva, and Boksler 2020), OECD, and EU-28 countries . Waste forecasting is helpful for planning and budget allocation ahead of waste management initiatives.…”

Section: Introductionmentioning

confidence: 99%

Demographic and socio-economic factors including sustainability related indexes in waste generation and recovery

Fan

Klemeš

Lee

et al. 2021

Energy Sources, Part A: Recovery, Utilization, and Environmenta

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There has been plenty of research on the influence of various socio-economic and demographic data on waste generation to develop effective and targeted waste reduction measures, including energy recovery. This study evaluates the relationship between the waste generation and Circular Material Use rate, Environmental Tax Revenue, and Global Innovation Index beyond the typical socio-economic factors (e.g., gross domestic product or population). Correlation analysis is conducted on the EU-27 datasets before the development of the predictive model. The correlation strength between the factors is discussed to identify the potential rebound effect from the central driver of economic growth and development. A positive correlation and partial rebound effect are identified in the data. The waste amount ending in disposal and energy recovery treatment increases with the Circular Material Use rate, suggesting that the expected gains from Circular Material Use rate are offset by other socio-economic factors such as increasing population or gross domestic product. However, a diminishing trend is observed in the rebound effect over the years. Multiple linear regression with validation is applied to identify the best fit model for predicting waste generation. Using population, gross domestic product, Circular Material Use rate, and Environmental Tax Revenues as independent variables, a model is generated with a mean absolute percentage error of 18.65% (7% lower than the benchmark) and R 2 (coefficient of determination) of 0.995.

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Comparative study of municipal solid waste disposal in three Chinese representative cities

Cited by 59 publications

References 36 publications

Analytical Methodology for the Identification of Critical Zones on the Generation of Solid Waste in Large Urban Areas

Analytical Methodology for the Identification of Critical Zones on the Generation of Solid Waste in Large Urban Areas

Fractional derivative multivariable grey model for nonstationary sequence and its application

Demographic and socio-economic factors including sustainability related indexes in waste generation and recovery

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