A comprehensive risk management framework for NIMBY projects: Integrating social network analysis and risk transmission chains

• Grounded theory and social network analysis integration for NIMBY risk assessment. • Identification of 17 key NIMBY risk factors from 29 case studies across 6 categories. • Risk network model reveals complex interactions and transmission pathways. • Simulation proves effectiveness of targeted interventions over random approaches. • Adaptive NIMBY risk management framework with compensation mechanisms proposed. Urban development projects often face local opposition, known as the NIMBY (Not in my back yard) effect, affecting infrastructure implementation and urban growth. This study aims to develop a systematic risk management approach for NIMBY projects. Employing grounded theory, 29 NIMBY cases in China were analyzed, identifying 17 risk factors across six categories. Social network analysis revealed risk interactions, highlighting four factors: government decision-making risk, government credibility risk, project completion risk, and online public opinion risk. Two risk transmission chains were delineated, each consisting of 4–5 interconnected risk factors, providing insights into risk propagation mechanisms. Simulation results indicated that targeted risk mitigation strategies based on these chains affected network efficiency differently compared to random approaches. The network efficiency reduced from 16.5% to 11.2% for the first chain-based strategy and to 13.4% for the second, while random interventions showed limited impact on network efficiency. Based on these findings, a NIMBY risk management system focusing on risk nodes and incorporating risk compensation mechanisms is proposed. This system addresses proactive governance, stakeholder engagement, and risk mitigation. The research contributes to the understanding and management of NIMBY risks, providing tools for policymakers and project managers. The findings suggest that this approach may affect NIMBY conflicts and community acceptance of urban development projects, potentially influencing urban development processes.

Authors

Institutions

Publication Details

Journal
Ecological Indicators
Published
2024-12-01
DOI
https://doi.org/10.1016/j.ecolind.2024.112942
Citations
2
Primary Topic
Community Development and Social Impact
Type
article
Field-Weighted Citation Impact
1.08

Funders

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

A comprehensive risk management framework for NIMBY projects: Integrating social network analysis and risk transmission chains

Huan Zhou, Ling Ran, Rui Miao, Genyu Xu et al.
2 citations
Ecological Indicators
Community Development and Social Impact
1.08
article

A comprehensive risk management framework for NIMBY projects: Integrating social network analysis and risk transmission chains

Huan Zhou, Ling Ran, Rui Miao, Genyu Xu, Ming Yang, Jian Xu, Ruiqu Ma, Jing Yu, Huixuan Xiang
article en
2 citations

Abstract

• Grounded theory and social network analysis integration for NIMBY risk assessment. • Identification of 17 key NIMBY risk factors from 29 case studies across 6 categories. • Risk network model reveals complex interactions and transmission pathways. • Simulation proves effectiveness of targeted interventions over random approaches. • Adaptive NIMBY risk management framework with compensation mechanisms proposed. Urban development projects often face local opposition, known as the NIMBY (Not in my back yard) effect, affecting infrastructure implementation and urban growth. This study aims to develop a systematic risk management approach for NIMBY projects. Employing grounded theory, 29 NIMBY cases in China were analyzed, identifying 17 risk factors across six categories. Social network analysis revealed risk interactions, highlighting four factors: government decision-making risk, government credibility risk, project completion risk, and online public opinion risk. Two risk transmission chains were delineated, each consisting of 4–5 interconnected risk factors, providing insights into risk propagation mechanisms. Simulation results indicated that targeted risk mitigation strategies based on these chains affected network efficiency differently compared to random approaches. The network efficiency reduced from 16.5% to 11.2% for the first chain-based strategy and to 13.4% for the second, while random interventions showed limited impact on network efficiency. Based on these findings, a NIMBY risk management system focusing on risk nodes and incorporating risk compensation mechanisms is proposed. This system addresses proactive governance, stakeholder engagement, and risk mitigation. The research contributes to the understanding and management of NIMBY risks, providing tools for policymakers and project managers. The findings suggest that this approach may affect NIMBY conflicts and community acceptance of urban development projects, potentially influencing urban development processes.

Ecological IndicatorsVol. 169
Yunnan Agricultural University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 19%
Community Development and Social Impact
1.08
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.