Environmental benefits of airport waste classification and recycling strategies: An analysis of behavior in multi-agent systems

• The classification and recycling of airport waste, as a key point in developing green airports, involves many subjects and complex influence factors. • Multi-agent system of airport waste classification and recycling is constructed to analyze the effects of different policy strategies. • Implementing policy strategies can significantly change passengers’ waste disposal behavior and more than double their willingness to use green products. • The full-factor change scenario demonstrates a significant effect in optimizing the waste structure and also has a substantial environmental benefit. The classification and recycling of airport waste has become a key point in developing green airports. However, airport waste classification involves a large number of stakeholders. The influence factors are complex, and the interactions between the stakeholders are not clear. Based on the agent-based model, we sort out the behaviors and driving factors of the three main stakeholders, namely passengers, collectors, and managers, and construct a multi-agent system of airport waste classification and recycling with the coupling of the waste generation subsystem, the recycling subsystem and the strategy formulation subsystem. By the complex system, we analyze the comprehensive effects of different policy strategies. Using the life cycle assessment method, an environmental benefit assessment model for airport waste classification and recycling is established to analyze the environmental benefits of different strategies and find the best strategy for the airport. The results show that implementing policy strategies can significantly change passengers’ waste disposal behavior and more than double their willingness to use green products. The combined strategy of the full-factor change scenario demonstrates a significant effect in optimizing the waste metabolism structure and recyclables disposal structure at the airport, with a 76.9 % increase in the use of green products relative to the BAU scenario. Through the implementation of the combined strategy, recyclable waste recycling can reduce 10.6 t CO 2 e and 15.6 % of photochemical oxidant emissions, which is a significant environmental benefit.

Authors

Institutions

Publication Details

Journal
Resources Conservation & Recycling Advances
Published
2024-12-11
DOI
https://doi.org/10.1016/j.rcradv.2024.200239
Citations
4
Primary Topic
Aviation Industry Analysis and Trends
Type
article
Field-Weighted Citation Impact
3.31

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Environmental benefits of airport waste classification and recycling strategies: An analysis of behavior in multi-agent systems

Yufeng Wu, Yi Yi, Zhibo Zhao, Yifan Gu et al.
4 citations
Resources Conservation & Recycling Advances
Aviation Industry Analysis and Trends
3.31
article

Environmental benefits of airport waste classification and recycling strategies: An analysis of behavior in multi-agent systems

Yufeng Wu, Yi Yi, Zhibo Zhao, Yifan Gu, Mingxuan Tuo, You-jeong Hong, Yihan Li, Xinrui Guo, Zixin Bian
article en
4 citations

Abstract

• The classification and recycling of airport waste, as a key point in developing green airports, involves many subjects and complex influence factors. • Multi-agent system of airport waste classification and recycling is constructed to analyze the effects of different policy strategies. • Implementing policy strategies can significantly change passengers’ waste disposal behavior and more than double their willingness to use green products. • The full-factor change scenario demonstrates a significant effect in optimizing the waste structure and also has a substantial environmental benefit. The classification and recycling of airport waste has become a key point in developing green airports. However, airport waste classification involves a large number of stakeholders. The influence factors are complex, and the interactions between the stakeholders are not clear. Based on the agent-based model, we sort out the behaviors and driving factors of the three main stakeholders, namely passengers, collectors, and managers, and construct a multi-agent system of airport waste classification and recycling with the coupling of the waste generation subsystem, the recycling subsystem and the strategy formulation subsystem. By the complex system, we analyze the comprehensive effects of different policy strategies. Using the life cycle assessment method, an environmental benefit assessment model for airport waste classification and recycling is established to analyze the environmental benefits of different strategies and find the best strategy for the airport. The results show that implementing policy strategies can significantly change passengers’ waste disposal behavior and more than double their willingness to use green products. The combined strategy of the full-factor change scenario demonstrates a significant effect in optimizing the waste metabolism structure and recyclables disposal structure at the airport, with a 76.9 % increase in the use of green products relative to the BAU scenario. Through the implementation of the combined strategy, recyclable waste recycling can reduce 10.6 t CO 2 e and 15.6 % of photochemical oxidant emissions, which is a significant environmental benefit.

Resources Conservation & Recycling AdvancesVol. 25
Beijing University of Technology (CN)
Beijing Social Science Fund, Beijing Municipal Social Science Foundation
Openalex Percentile: Top 7%
Aviation Industry Analysis and Trends
3.31
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.