Study on the Vulnerability of Municipal Solid Waste Resource Symbiosis Network—A Case Study Based on the Construction of Zero Waste City in Panjin

Abstract

Building zero-waste cities is an important initiative. It helps China to meet the challenges of climate change, build an ecological civilization, and build a beautiful and high-quality China. Through the recycling and safe disposal of waste, the ultimate goal of the construction of zero-waste cities is to minimize the amount of solid waste. Municipal Solid Waste Resource Symbiosis Network (MSWRSN) is the best way to achieve zero-waste cities. However, this system is vulnerable to many factors. These factors will restrict the construction and development of zero-waste cities. This study proposes a vulnerability measurement method of MSWRSN based on energy exchange. Panjin, a city in Liaoning Province, is used as an example to simulate it. The vulnerability measurement method of MSWRSN is composed of three parts. First, the study adopts a directional weighting approach to design the topologic structure of MSWRSN. Second, Dijkstra’s algorithm is used to analyze two topological parameters, node mesonumber and edge mesonumber. It also focuses on single-node failure mode and edge failure mode. Finally, the study uses a functional measure function to calculate the vulnerability of each node and each edge in symbiotic networks. The results of the study show that (1) MSWRSN in Panjin has not yet formed a nested pattern. The symbiotic patterns of different industrial chains are also different. (2) Node failure has a greater impact on MSWRSN. (3) There are differences in the vulnerability of the industry chain in the network. Based on the findings of the study, this study advises managing the vulnerability of MSWRSN from the following aspects. It includes increasing the diversity of symbiotic units, promoting the development of symbiotic networks to nested types, and strengthening risk monitoring and management of core enterprises.

Keywords

• solid waste
• resourceful treatment
• symbiotic network
• simulation experiments