Estimating the Carbon Emission of Construction Waste Recycling Using Grey Model and Life Cycle Assessment: A Case Study of Shanghai

Wang, Ting; Li, Kaiyi; Liu, Defu; Yang, Yang; Wang, Dong

doi:10.3390/ijerph19148507

Cited by 24 publications

(7 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the grey theory, the information in the prediction system is partially known, and there is an uncertain relationship between the parameters in the system. The advantages of the GM model are that the prediction accuracy is not affected by normally distributed data [17]. Several factors affect the amount of C&D waste generated.…”

Section: Short-term and Medium-term Prediction Methodsmentioning

confidence: 99%

Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Fang,

Shi,

Chen

et al. 2024

Sustainability

View full text Add to dashboard Cite

With rapid urban development, the “waste-free city” concept has emerged. Therefore, the accurate prediction of the amount of C&D waste is of great importance. However, many countries and regions, including China, have not yet established C&D waste databases and standard prediction methods. This study proposed a method using a weighted combination of the grey theory model (GM) and the autoregressive integrated moving average (ARIMA) model to predict the quantity of urban C&D waste in the future. Based on a case study in Guangzhou, this study compared the prediction results of three prediction models, namely the GM, the ARIMA, and the proposed weighted combined model of the GM and the ARIMA (GM-ARIMA). The results of this study proved that the proposed combined GM-ARIMA model had a better predictive performance than both the separated models. The mean absolute percentage errors (MAPE) of the GM and ARIMA models were 12.11% and 14.26%, respectively, whereas the proposed GM-ARIMA model had a lower MAPE (8.5%). This study found that the generation of C&D waste in Guangzhou will continue to grow steadily. From 2024 to 2035, the quantity of C&D waste is expected to reach 850 million tons cumulatively, with an annual growth rate of 7.1%.

show abstract

Section: Short-term and Medium-term Prediction Methodsmentioning

confidence: 99%

Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Fang,

Shi,

Chen

et al. 2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Ref. [123] assesses the waste generated by newly built buildings in Shanghai. Building waste management can significantly reduce carbon emissions.…”

Section: Research Frontiersmentioning

confidence: 99%

Scientometric Analysis and Visualization of Carbon Emission Studies in the Construction Industry

Luo,

Yang,

Huang

et al. 2024

Buildings

View full text Add to dashboard Cite

The field of carbon emissions in the construction industry has drawn extensive attention from researchers and practitioners due to the issue of global warming. In this study, an in-depth analysis of the research status, trends, and frontiers in the field of carbon emissions in the construction industry was carried out. The CiteSpace tool was used to visualize and analyze relevant papers from 1985 to 2023, to describe the overall knowledge structure in the field of carbon emissions in the construction industry using dual-map overlay analysis, journal co-citation network analysis, and keyword co-occurrence network analysis, to apply cluster analysis and burst detection to identify research trends in the field and the frontiers, and to analyze the scientific collaborations in the field. Further, the core issues in the field of carbon emissions in the construction industry were explored and relevant recommendations were proposed. The results are of great significance in identifying and analyzing knowledge systems and research patterns in the field of carbon emissions in the construction industry and help us to discover and understand the current deficiencies, trends, and frontiers in this field, thus providing useful suggestions and reflections for policymakers, practitioners, researchers, and other stakeholders.

show abstract

“…The costs are considered based on the data attained from Annexure 17.1 of Chapter 17 of the Central Public Health and Environmental Engineering Organization (CPHEEO) and Nagar Nigam reports [72]. Inputs regarding carbon emission were derived from the following resources [73] and [74], as these work gives estimation of emission in landfills mining. All the data considered in the model is taken as fuzzy to accommodate discrepancies caused by geographical location and time.…”

Section: Numerical Experiments With Real Life Case Studymentioning

confidence: 99%

An Innovative Algorithm-Driven Optimization Framework for Landfill Mining: Aiming Sustainable Profitability Expeditiously

Bhakuni,

Das

2023

IEEE Access

View full text Add to dashboard Cite

Landfill mining (LFM) offers a potential solution to the environmental issues associated with landfilling. The current work aims to develop an efficient optimization framework for LFM that is sustainable, profit-yielding, and time minimizing at the same instance. The proposed framework involves a multi-objective multi-level solid transportation model (MOMLSTM). This model can be adapted by the organization across various geographies as it incorporates uncertainty of all the parameters of time, cost, and emission through pentagonal fuzzy numbers (PFN). The other crucial contribution of this work is the development of a genetic algorithm for offspring refinement (GAOR) that contributes in optimizing multiobjective optimization (MOO) problems. The GAOR's performance has been verified using the Congress on evolutionary computation (CEC) 2020 multi-objective benchmark test functions. GAOR is assessed against six robust MOO algorithms, including the multi-objective equilibrium optimizer slime mould algorithm (MOEOSMA), enhanced multi-objective particle swarm optimization (EMOPSO), multi-objective gorilla troops optimizer (MOGTO), adaptive crossover strategy enhanced NSGA-II (ASDMSGA-II), multiobjective slime mould algorithm (MOSMA), and multi-objective equilibrium optimizer algorithm (MEOA). GAOR delivered outstanding results across three crucial performance indicators. To rank these algorithms, a Friedman test was conducted, and GAOR achieved the highest ranking among the tested MOO algorithms. A case study is considered for real-life application of the model and solution technique GAOR. The outcomes of MOMLSTM from GAOR are compared to the epsilon-constraint method. The comparison revealed noteworthy improvements: a 0.14% increase in profits, a 1.29% reduction in carbon emissions, and a 3.81% decrease in the time required.

show abstract

Estimating the Carbon Emission of Construction Waste Recycling Using Grey Model and Life Cycle Assessment: A Case Study of Shanghai

Cited by 24 publications

References 35 publications

Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Scientometric Analysis and Visualization of Carbon Emission Studies in the Construction Industry

An Innovative Algorithm-Driven Optimization Framework for Landfill Mining: Aiming Sustainable Profitability Expeditiously

Contact Info

Product

Resources

About