Cited by An integrated model of life cycle assessment and system dynamics for construction and demolition waste management and reduction in Italy

Building sustainability through a novel exploration of dynamic LCA uncertainty: Overview and state of the art DOI

Haidar Hosamo, Guilherme B. A. Coelho,

Elsa Buvik

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 264, P. 111922 - 111922

Published: Aug. 8, 2024

Life Cycle Assessment is necessary for evaluating the environmental impacts of buildings throughout their life cycle, considering factors such as energy consumption, emissions, and resource utilization. However, Dynamic introduces a temporal dimension, acknowledging that building's performance evolves due to technological advancements, occupancy behavior, changing conditions. This paper reviews DLCA, focusing on uncertainties arising from parameter, scenario, model variability, emphasizes integration technologies like Building Information Modeling, Internet Things, machine learning enhance real-time data collection predictive analytics. An extensive review 430 papers, refined 180, reveals 55 % publications are in sciences, with significant contributions United Kingdom (27.8 %), France (24.1 China (18.1 %). Key findings include variations embodied greenhouse gas emissions materials aluminum dynamic aspects transportation impacts, which extend beyond traditional metrics operational efficiency over time. Uncertainties all LCA stages (A1 D) addressed, service life, water use, needs. Advanced methodologies, including proposed framework hybrid approach integrates process-based input-output methods, suggested comprehensiveness assessments. The monitoring analytics further improves adaptability precision models, emphasizing necessity continuous updates scenario analyses capture future conditions accurately. study paves way research aimed at mitigating major sources uncertainty, promoting more sustainable building practices, advancing field LCA.

Language: Английский

Citations

Circular economy in agro food supply chain: Bibliometric and network analysis DOI

P. Banerjee, Deeksha Singh, Sambashiva Rao Kunja

et al.

Business Strategy & Development, Journal Year: 2024, Volume and Issue: 7(2)

Published: May 2, 2024

Abstract Recently, there has been growing interest among scholars and industry professionals in understanding how agro‐food supply chains can shift from a linear to circular model. This involves managing waste, recovering resources, adopting sustainable practices. However, knowledge on economies is scattered, with limited comprehensive studies available. To address this gap, our study conducts bibliometric network analysis of 364 documents the Scopus database. We identify influential authors, contributing countries journals, most cited documents. Additionally, we use visualize connections between authors keywords, providing insights into emerging trends critical research areas. also uncover important keywords themes, laying foundation for future directions. Overall, offers overview, serving as basis further exploration field.

Language: Английский

Citations

Interpretable machine learning method empowers dynamic life cycle impact assessment: A case study on the carcinogenic impact of coal power generation DOI

Shuo Wang, Tianzuo Zhang, Ziheng Li

et al.

Environmental Impact Assessment Review, Journal Year: 2025, Volume and Issue: 112, P. 107837 - 107837

Published: Jan. 22, 2025

Language: Английский

Citations

Environmental evaluation of emerging bakery waste oil-derived sophorolipids production by performing a dynamic life cycle assessment DOI

Yahui Miao, Xiaomeng Hu, Ming Ho To

et al.

Sustainable Production and Consumption, Journal Year: 2024, Volume and Issue: 47, P. 59 - 70

Published: March 24, 2024

Language: Английский

Citations

Use of process simulation to obtain life cycle inventory data for LCA: A systematic review DOI

Jannatul Ferdous, Farid Bensebaa, Kasun Hewage

et al.

Cleaner Environmental Systems, Journal Year: 2024, Volume and Issue: 14, P. 100215 - 100215

Published: July 24, 2024

Life Cycle Inventory (LCI) analysis is an essential and time-consuming phase of life cycle assessment (LCA). While primary data among the most reliable desirable source types, it often challenging to collect for industry-specific processes due confidentiality concerns, in particular with respect unique proprietary processes. In such cases, computer-based process simulation software can be used fill gaps inventory based on mass energy balances. building models, engagement industry verification models validation simulated data. Although simulation-based modelling not a new research area, there has been no systematic review this topic common methodological choices. To gap, aims identify practices simulating LCI using simulation. Studies that were reviewed reasons simulation, approaches LCI, employed, processes, calculate report uncertainty. Based findings, framework was proposed explain how integrated conventional LCA, specifically industrial

Language: Английский

Citations

Comparison of mesophilic and thermophilic anaerobic digestion of food waste: Focusing on methanogenic performance and pathogens removal DOI

Shaojie Bi,

Chunshuang Wang,

Haipeng Wang

et al.

Renewable Energy, Journal Year: 2024, Volume and Issue: 233, P. 121184 - 121184

Published: Aug. 14, 2024

Language: Английский

Citations

Microbial process in anaerobic digestion of food wastes for biogas production: a review DOI

Satchidananda Mishra,

Amrita Banerjee, Sourav Chattaraj

et al.

Systems Microbiology and Biomanufacturing, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

Language: Английский

Citations

An integrated model of life cycle assessment and system dynamics for construction and demolition waste management and reduction in Italy DOI

Yanqing Yi, Xunchang Fei, Maria Cristina Lavagnolo

et al.

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: unknown, P. 144469 - 144469

Published: Dec. 1, 2024

Language: Английский

Citations