Version 1
: Received: 9 October 2024 / Approved: 10 October 2024 / Online: 10 October 2024 (09:09:53 CEST)
How to cite:
Wang, H.; S.Sua, L. Urban Resilience Amid Supply Chain Disruptions: A Causal and Cointegration-Based Risk Model for G-7 Cities Post-COVID-19. Preprints2024, 2024100759. https://doi.org/10.20944/preprints202410.0759.v1
Wang, H.; S.Sua, L. Urban Resilience Amid Supply Chain Disruptions: A Causal and Cointegration-Based Risk Model for G-7 Cities Post-COVID-19. Preprints 2024, 2024100759. https://doi.org/10.20944/preprints202410.0759.v1
Wang, H.; S.Sua, L. Urban Resilience Amid Supply Chain Disruptions: A Causal and Cointegration-Based Risk Model for G-7 Cities Post-COVID-19. Preprints2024, 2024100759. https://doi.org/10.20944/preprints202410.0759.v1
APA Style
Wang, H., & S.Sua, L. (2024). Urban Resilience Amid Supply Chain Disruptions: A Causal and Cointegration-Based Risk Model for G-7 Cities Post-COVID-19. Preprints. https://doi.org/10.20944/preprints202410.0759.v1
Chicago/Turabian Style
Wang, H. and Lutfu S.Sua. 2024 "Urban Resilience Amid Supply Chain Disruptions: A Causal and Cointegration-Based Risk Model for G-7 Cities Post-COVID-19" Preprints. https://doi.org/10.20944/preprints202410.0759.v1
Abstract
The COVID-19-induced strain on global supply chains led to significant market imbalances and unprecedented inflation, particularly affecting urban economies. Containment policies and stimulus packages resulted in unpredictable demand shifts, challenging urban supply chain planning and resource distribution. These disruptions underscored the need for robust risk management models, especially in cities where economic activity and population density exacerbate supply chain vulnerabilities. This study develops a comprehensive risk model tailored for G-7 urban economies, analyzing the causal and cointegration relationships between key economic indicators. Using Granger causality tests and a factor-augmented vector autoregression (FAVAR) approach, the study examines complex time series and high-dimensional variables, focusing on urban-specific indicators such as the Composite Leading Indicator (CLI) and Business Confidence Indicator (BCI). Our results indicate strong causal relationships among these indicators, validating CLI as a reliable early predictor of urban economic trends. The findings confirm the viability of this urban supply chain risk management model, offering potential pathways for strengthening urban resilience and economic sustainability in the face of future disruptions. This approach positions the study within the context of urban science, emphasizing the impacts on cities and how urban economies can benefit from the developed risk model.
Keywords
supply chain risk; VAR; FAVAR; causality analysis
Subject
Business, Economics and Management, Business and Management
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
