Measuring the Green Infrastructure Network Resilience: Based on Disturbance Scenario Simulations

With rapid urban expansion and frequent natural disasters, green infrastructure (GI) networks are highly susceptible to disturbances and impacts. Improving the resilience of GI networks is important for maintaining species migration, improving ecological efficiency, and realizing sustainable development. Therefore, an effective GI network resilience measure must be constructed to measure it more accurately. There is a lack of current research that utilizes dynamic simulation methods to measure GI network resilience. This paper selects Jinan, China as the study area, uses morphological spatial pattern analysis and landscape connectivity analysis methods to select GI sources, and uses minimum cumulative resistance and the gravity model to construct the GI network. We construct the evaluation index of GI network resilience. Using this index to evaluate the GI network resilience to changes under various disturbance scenarios. The results show that the network resilience level is good. Networks exhibit different levels of resilience under random and intentional attack conditions. The nodes in the top 10% of the node degree value are critical to maintaining the operation of the network system. This paper expected that the network resilience evaluation method could be enriched and provide references for decision-makers to formulate sustainable urban development strategies.

Authors

Institutions

Publication Details

Journal
Ecosystem Health and Sustainability
Published
2025-01-01
DOI
https://doi.org/10.34133/ehs.0337
Citations
1
Primary Topic
Infrastructure Resilience and Vulnerability Analysis
Type
article
Field-Weighted Citation Impact
0.95
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Measuring the Green Infrastructure Network Resilience: Based on Disturbance Scenario Simulations

Jialu Shi, Chengxin Wang, Xuan Wang, Xumin Jiao
1 citations
Ecosystem Health and Sustainability
Infrastructure Resilience and Vulnerability Analysis
0.95
article

Measuring the Green Infrastructure Network Resilience: Based on Disturbance Scenario Simulations

Jialu Shi, Chengxin Wang, Xuan Wang, Xumin Jiao
article en
1 citations

Abstract

With rapid urban expansion and frequent natural disasters, green infrastructure (GI) networks are highly susceptible to disturbances and impacts. Improving the resilience of GI networks is important for maintaining species migration, improving ecological efficiency, and realizing sustainable development. Therefore, an effective GI network resilience measure must be constructed to measure it more accurately. There is a lack of current research that utilizes dynamic simulation methods to measure GI network resilience. This paper selects Jinan, China as the study area, uses morphological spatial pattern analysis and landscape connectivity analysis methods to select GI sources, and uses minimum cumulative resistance and the gravity model to construct the GI network. We construct the evaluation index of GI network resilience. Using this index to evaluate the GI network resilience to changes under various disturbance scenarios. The results show that the network resilience level is good. Networks exhibit different levels of resilience under random and intentional attack conditions. The nodes in the top 10% of the node degree value are critical to maintaining the operation of the network system. This paper expected that the network resilience evaluation method could be enriched and provide references for decision-makers to formulate sustainable urban development strategies.

Ecosystem Health and SustainabilityVol. 11
Yellow River Institute of Hydraulic Research (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 27%
Infrastructure Resilience and Vulnerability Analysis
0.95
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.