Spatio-temporal pattern and driving factors of municipal solid waste generation in China: New evidence from exploratory spatial data analysis and dynamic spatial models

Wang, Kaifeng; Zhao, Xinquan; Peng, Biyu; Zeng, Yongmei

doi:10.1016/j.jclepro.2020.121794

Cited by 26 publications

(5 citation statements)

References 20 publications

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“…The scatter chart in Supplementary Figure 6 showed that most cities with a large population have higher per capita MSW generation, while some cities located in the Mid-region and Eastern region, such as Handan in the Eastern region and Nanyang in the Mid-Region, have higher populations and lower economic development levels (see Supplementary Figure 4a,b,g,h), which leads to a lower MSW generation per capita . It would be contrary to the trend in the developed regions, resulting in no significant correlation in the whole country based on the panel data in the city level.…”

Section: Resultsmentioning

confidence: 99%

Air Pollutant Emission Inventory of Waste-to-Energy Plants in China and Prediction by the Artificial Neural Network Approach

Cui

Boré

et al. 2022

Environ. Sci. Technol.

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The waste-to-energy (WTE) plant has been deployed in 205 cities in China. However, it always faces public resistance to be built because of the great concerns on flue gas pollutants (FGPs). There are limited studies on the socioeconomic heterogeneity analysis and prediction models of WTE capacity/ FGP emission inventories (EIs) based on big data. In this study, the incinerator level emission factors (EFs) in 2020 of PM, SO2, NO x , CO, HCl, dioxins, Hg, Cd + Tl, and Sb + As+ Pb + Cr + Co + Cu + Mn + Ni were calculated based on 322,926 monitoring values of all the 481 WTE plants (1140 processing lines) operating in China, with uncertainties in the range of ±34.70%. The EFs were significantly 45–96% lower than the national standard (GB18485-2014) and had negative relationships with local socioeconomic elements, while WTE capacity and FGP EIs had significantly positive correlations. Gross domestic product, area of built district, and municipal solid waste generation were the main driving forces of WTE capacity. The WTE capacity increased by 150% from 2015 to 2020, while the total emission of PM, SO2, CO, dioxins, Hg, and Sb + As + Pb + Cr + Co + Cu + Mn + Ni decreased by 42.46–88.24%. The artificial neural network models were established to predict WTE capacity and FGP EIs in the city level, with the mean square errors ranging from 0.003 to 0.19 within the model validation limits. This study provides data and model support for the formulation of appropriate WTE plans and a pollutant emission control scheme in different economic regions.

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Section: Resultsmentioning

confidence: 99%

Air Pollutant Emission Inventory of Waste-to-Energy Plants in China and Prediction by the Artificial Neural Network Approach

Cui

Boré

et al. 2022

Environ. Sci. Technol.

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“…Themelis 4 HCl, PM, dioxin, and heavy metals (Hg, Cd+TI, Sb+As+Pb+Cr+Co+Cu+Mn+Ni). Furthermore, previous studies examined the driving factors of MSW generation in China 12 , the relationships between incineration amounts and factors such as economic size and population density at the provincial level 13 and the correlation between socioeconomic factors and city-level emission factors 11 . However, factors governing the emission trends of multiple air pollutants from the WTE industry after the introduction of the 2014 emission standard remain unclear.…”

Section: Growth Of the Wte Industry In China And Environmental Perfor...mentioning

confidence: 99%

Growth of the WTE industry in China and environmental performance

Ma,

Boré,

Cui

et al. 2024

Preprint

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In the last twenty years, the waste-to-energy (WTE) capacity of China has become greater than any other nation. This study examines the environmental performance of the Chinese WTE power plants, including greenhouse gas (GHG), SO2, NOx, HCl, dioxins, particulate matter (PM), and heavy metal (HMs, 11 types) emissions, from 2005 to 2020. The results showed that GHG, NOx, and HCl national emissions increased with increasing WTE capacity. In contrast, PM, CO, SO2, and dioxins peaked in 2015 and gradually declined by 26%, 33%, 28%, and 82%, respectively, in 2020. MSW generation intensity, GDP per capita, and population were factors driving up pollutant emissions, whereas enhancing emission control and MSW treatment structure were key to lower emissions. Between 2005 and 2020, emission factors decreased by 22%-96%. Replacing landfilling by combustion with energy recovery (WTE) reduced the carbon footprint by 137 million tons of CO2-eq in 2020. Limiting emissions of PM, SO2, and NOx in line with the ultra-low emission (ULE) requirements can result in a yearly health benefit of US$18.4 million.

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“…The generation rate and composition of MSW were closely related to various social and economic parameters in the community and the results showed that the middle socio-economic group produced the largest amount of solid waste. Wang et al [ 24 ] used the ESDA method to analyze the effects of population, green coverage rate, industrial structure, and road density on the amount of MSW generation, and found that population, technology, urbanization, and green coverage rate all have inhibitory effects on MSW generation. Industrial structure, number of hospital beds per capita, and road density are the driving factors of MSW generation.…”

Section: Literature Reviewmentioning

confidence: 99%

Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long Short-Term Memory Model

Liu

Zhang

Wang

et al. 2022

IJERPH

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The accurate prediction of Municipal Solid Waste (MSW) electricity generation is very important for the fine management of a city. This paper selects Shanghai as the research object, through the construction of a Bidirectional Long Short-Term Memory (BiLSTM) model, and chooses six influencing factors of MSW generation as the input indicators, to realize the effective prediction of MSW generation. Then, this study obtains the MSW electricity generation capacity in Shanghai by using the aforementioned prediction results and the calculation formula of theMSW electricity generation. The experimental results show that, firstly, the mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE) values of the BiLSTM model are 42.31, 7.390, and 63.32. Second, it is estimated that by 2025, the maximum and minimum production of MSW in Shanghai will be 17.35 million tons and 8.82 million tons under the three scenarios. Third, it is predicted that in 2025, the maximum and minimum electricity generation of Shanghai MSW under the three scenarios will be 512.752 GWh/y and 260.668 GWh/y. Finally, this paper can be used as a scientific information source for environmental sustainability decision-making for domestic MSW electricity generation technology.

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Spatio-temporal pattern and driving factors of municipal solid waste generation in China: New evidence from exploratory spatial data analysis and dynamic spatial models

Cited by 26 publications

References 20 publications

Air Pollutant Emission Inventory of Waste-to-Energy Plants in China and Prediction by the Artificial Neural Network Approach

Air Pollutant Emission Inventory of Waste-to-Energy Plants in China and Prediction by the Artificial Neural Network Approach

Growth of the WTE industry in China and environmental performance

Electricity Generation Forecast of Shanghai Municipal Solid Waste Based on Bidirectional Long Short-Term Memory Model

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