Forecasting municipal solid waste in Lithuania by incorporating socioeconomic and geographical factors

Paulauskaitė-Tarasevičienė, Agnė; Raudonis, Vidas; Šutienė, Kristina

doi:10.1016/j.wasman.2022.01.004

Cited by 22 publications

(3 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are more factors affecting county solid waste harmless disposal capacity. Drawing on the studies of related scholars (Chu et al, 2016;Spoann et al, 2018;Chu et al, 2020;Paulauskaite-Taraseviciene et al, 2022), this paper focuses on macro factors such as the construction of public facilities, regional economic development, population density, urbanization and investment in fixed assets for waste disposal. 1) Public facilities construction.…”

Section: Variablesmentioning

confidence: 99%

Spatial and temporal evolution and driving factors of county solid waste harmless disposal capacity in China

Chen

Xing

Zhou

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

Currently, China mainly adopts the waste treatment model of “household sorting, village collection, town transfer and county disposal.” Determining the spatial and temporal distribution of China’s county solid waste harmless disposal capacity and formulating strategies according to local conditions are of great significance in promoting the construction of beautiful villages in China and realizing the Beautiful China strategy. This paper explores the spatial and temporal evolution characteristics of county solid waste harmless disposal capacity by selecting relevant data from 27 provinces in China from 2006 to 2020, and adopts the Dagum Gini coefficient method to measure the spatial gap of it. In addition, this paper empirically analyses the drivers affecting county solid waste harmless disposal capacity using the spatial Durbin model (SDM). The main conclusions are as follows: 1) In terms of time, county solid waste harmless disposal capacity in China as a whole shows a year-by-year increasing trend, especially after 2018 when the growth rate is faster. 2) In terms of spatial patterns, the solid waste harmless disposal capacity of coastal areas is generally higher than that of inland areas, and the distribution of provinces with low and middle levels of solid waste harmless disposal capacity is characterized by concentrated contiguity. From the perspective of spatial agglomeration, the characteristics of spatial agglomeration in the north are gradually becoming more pronounced, while those in the south are not significant. From the trajectory of the evolution of the spatial center of gravity, the center of gravity of county solid waste harmless disposal capacity as a whole shows a northeast, then northwest, then northeast movement, and the speed of “northward expansion” is greater than the speed of “eastward expansion”. 3) The results of the Dagum Gini coefficient and its decomposition show that the northeast has the smallest average annual rate of change in the Gini coefficient. The reduction of the within-group gap is an important driver towards equilibrium. The contribution of hypervariable density is decreasing year by year. 4) The number of harmless disposal plants, GDP per person, population urbanization, the number of township waste transfer stations and county waste disposal fixed asset investment are important drivers of county waste harmless disposal capacity. Findings provide helpful insights into optimizing rural habitat and promoting the comprehensive transformation of China’s county development.

show abstract

Section: Variablesmentioning

confidence: 99%

Spatial and temporal evolution and driving factors of county solid waste harmless disposal capacity in China

Chen

Xing

Zhou

et al. 2023

Front. Environ. Sci.

View full text Add to dashboard Cite

show abstract

“…Municipal solid waste (MSW) management, defined as the management of domestic and commercial garbage generated under the jurisdiction of a municipal body [ 2 ] is not an exception. Waste disposal and waste handling are global problems [ 3 ] and many countries are implementing mitigation strategies in MSW to reduce greenhouse gas (GHG) emissions [ 4 ]. Additionally, MSW management is called to play a relevant role in the circular economy because landfills as end-of-life product receivers could promote remanufacturing and zero-waste implementations.…”

Section: Introductionmentioning

confidence: 99%

Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method

Solaz¹,

Artacho-Ramírez²,

Aragonés‐Beltrán³

et al. 2023

Heliyon

View full text Add to dashboard Cite

“…Many socio-economic, demographic, or geographical factors have been used to determine which of these has the most significant relationship with MSW [3]. In general, the most commonly used indicators are population, income, type of population, age groups, employment, electricity consumption, consumer price index (CPI), gross domestic product (GDP) gross domestic spending on research and development (R&D) [4]. Some researches additionally include education [5], culture [6], territorial (regional) characteristics [7] or climate indicators [8].…”

Section: Introductionmentioning

confidence: 99%

ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania

Raudonis

Paulauskaitė-Tarasevičienė

Eidimtas³

2022

Sustainability

Self Cite

View full text Add to dashboard Cite

Waste management is currently a fast-growing environmental business and one of solutions to manage the huge amount of waste being generated on landfills is to use the disposed waste as an energy source. There is a major focus on energy forecasting, highlighting the importance of having reliable data on the volume and composition of municipal solid waste in landfills. However, the lack of historical data is forcing the development of machine-learning based models. This study contributes to this field by proposing a hybrid ANN-based model to forecast the total amount of landfill waste, different waste fraction and the potential for energy recovery. The proposed model includes an adaptive number of inputs adjusted to the relevant waste fraction and to the specific landfill. The obtained results substantiated that the proposed model allows for stable and accurate forecasting of recovered energy potential in cases where there is insufficient historical data. The experiments showed that the model with 12 inputs (meaning the forecast of the future value takes into account the last 12 months of data) was the most accurate in the energy forecasting task, with the lowest forecasting error in terms of mean absolute error −8.9878 gigawatt hours per year.

show abstract

Forecasting municipal solid waste in Lithuania by incorporating socioeconomic and geographical factors

Cited by 22 publications

References 43 publications

Spatial and temporal evolution and driving factors of county solid waste harmless disposal capacity in China

Spatial and temporal evolution and driving factors of county solid waste harmless disposal capacity in China

Sustainable selection of waste collection trucks considering feasible future scenarios by applying the stratified best and worst method

ANN Hybrid Model for Forecasting Landfill Waste Potential in Lithuania

Contact Info

Product

Resources

About