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Assessing Shock Propagation and Cascading Uncertainties Using the Input–Output Framework: Analysis of an Oil Refinery Accident in Singapore

Author

Listed:
  • Pradeep V. Mandapaka

    (Institute of Catastrophe Risk Management, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore)

  • Edmond Y. M. Lo

    (Institute of Catastrophe Risk Management, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore
    School of Civil and Environmental Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore 639798, Singapore)

Abstract

The impacts of shock events frequently cascade beyond the primarily affected sector(s), through the interdependent economic system, and result in higher-order indirect losses in other sectors. This study employed the inoperability input–output model (IIM) and the dynamic IIM (DIIM) to model recovery of sectors after a shock event and quantify associated total losses. Considering data limitations and uncertainties regarding sectoral recovery time, a key variable in DIIM, a probabilistic approach is used for modelling uncertainty in recovery times. The event analyzed is the 2011 oil refinery fire accident in Pulau Bukom (PB) island, Singapore, which caused the refinery to shut down for 11 days and be partially operational for several days thereafter. The impacts are assessed using the regrouped 15-sector Singapore IO data of year 2010, with manufacturing sector as the directly affected sector. The initial economic impact of the PB refinery fire is assessed in the top-down framework using the refinery’s contribution to the manufacturing sector and nation’s GDP. The higher-order losses are quantified considering different recovery paths for the directly affected sector and accounting for its inventory. Simulation experiments using synthetic IO tables are also carried out to understand relationship between recovery characteristics of directly and indirectly affected sectors. The results from IIM analysis show that the indirect losses are about 35–38% of direct losses. The DIIM analysis reveal that the utilities sectors (e.g., electricity, water supply and treatment) suffer the largest inoperability among indirectly affected sectors for a given direct damage to the manufacturing sector. The results also illustrate the dependence of overall losses on the recovery path of the directly affected sector, and associated uncertainties in sectoral recovery times.

Suggested Citation

  • Pradeep V. Mandapaka & Edmond Y. M. Lo, 2023. "Assessing Shock Propagation and Cascading Uncertainties Using the Input–Output Framework: Analysis of an Oil Refinery Accident in Singapore," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1739-:d:1037982
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    1. Stéphane Hallegatte & Valentin Przyluski, 2010. "The Economics of Natural Disasters," CESifo Forum, ifo Institute - Leibniz Institute for Economic Research at the University of Munich, vol. 11(02), pages 14-24, July.
    2. Lei Zhou & Zhenhua Chen, 2021. "Are CGE models reliable for disaster impact analyses?," Economic Systems Research, Taylor & Francis Journals, vol. 33(1), pages 20-46, January.
    3. Adam Rose & Gauri-Shankar Guha, 2004. "Computable General Equilibrium Modeling of Electric Utility Lifeline Losses from Earthquakes," Advances in Spatial Science, in: Yasuhide Okuyama & Stephanie E. Chang (ed.), Modeling Spatial and Economic Impacts of Disasters, chapter 7, pages 119-141, Springer.
    4. Amine El Haimar & Joost R. Santos, 2014. "Modeling Uncertainties in Workforce Disruptions from Influenza Pandemics Using Dynamic Input‐Output Analysis," Risk Analysis, John Wiley & Sons, vol. 34(3), pages 401-415, March.
    5. Xue Jin & U. Rashid Sumaila & Kedong Yin, 2020. "Direct and Indirect Loss Evaluation of Storm Surge Disaster Based on Static and Dynamic Input-Output Models," Sustainability, MDPI, vol. 12(18), pages 1-25, September.
    6. Aijun Hu & Wei Xie & Ning Li & Xuanhua Xu & Zhonghui Ji & Jidong Wu, 2014. "Analyzing regional economic impact and resilience: a case study on electricity outages caused by the 2008 snowstorms in southern China," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 70(2), pages 1019-1030, January.
    7. Adam Rose & Shu‐Yi Liao, 2005. "Modeling Regional Economic Resilience to Disasters: A Computable General Equilibrium Analysis of Water Service Disruptions," Journal of Regional Science, Wiley Blackwell, vol. 45(1), pages 75-112, February.
    8. Kenneth G. Crowther & Yacov Y. Haimes & Gideon Taub, 2007. "Systemic Valuation of Strategic Preparedness Through Application of the Inoperability Input‐Output Model with Lessons Learned from Hurricane Katrina," Risk Analysis, John Wiley & Sons, vol. 27(5), pages 1345-1364, October.
    9. Jan Oosterhaven & Maaike C. Bouwmeester, 2016. "A New Approach To Modeling The Impact Of Disruptive Events," Journal of Regional Science, Wiley Blackwell, vol. 56(4), pages 583-595, September.
    10. Samiul Hasan & Greg Foliente, 2015. "Modeling infrastructure system interdependencies and socioeconomic impacts of failure in extreme events: emerging R&D challenges," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 78(3), pages 2143-2168, September.
    11. Olaf Jonkeren & Georgios Giannopoulos, 2014. "Analysing Critical Infrastructure Failure With A Resilience Inoperability Input--Output Model," Economic Systems Research, Taylor & Francis Journals, vol. 26(1), pages 39-59, March.
    12. Andre F. T. Avelino & Sandy Dall'erba, 2019. "Comparing the Economic Impact of Natural Disasters Generated by Different Input–Output Models: An Application to the 2007 Chehalis River Flood (WA)," Risk Analysis, John Wiley & Sons, vol. 39(1), pages 85-104, January.
    13. Pant, Raghav & Barker, Kash & Zobel, Christopher W., 2014. "Static and dynamic metrics of economic resilience for interdependent infrastructure and industry sectors," Reliability Engineering and System Safety, Elsevier, vol. 125(C), pages 92-102.
    14. Jie Zhang & Meng Lu & Lulu Zhang & Yadong Xue, 2021. "Assessing indirect economic losses of landslides along highways," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(3), pages 2775-2796, April.
    15. Hallegatte, Stephane & Przyluski, Valentin, 2010. "The economics of natural disasters : concepts and methods," Policy Research Working Paper Series 5507, The World Bank.
    16. Joost R. Santos & Mark J. Orsi & Erik J. Bond, 2009. "Pandemic Recovery Analysis Using the Dynamic Inoperability Input‐Output Model," Risk Analysis, John Wiley & Sons, vol. 29(12), pages 1743-1758, December.
    17. Hallegatte,Stephane, 2015. "The indirect cost of natural disasters and an economic definition of macroeconomic resilience," Policy Research Working Paper Series 7357, The World Bank.
    18. Louis De Mesnard, 2009. "Is The Ghosh Model Interesting?," Journal of Regional Science, Wiley Blackwell, vol. 49(2), pages 361-372, May.
    19. Chen, Hao & Yan, Haobo & Gong, Kai & Geng, Haopeng & Yuan, Xiao-Chen, 2022. "Assessing the business interruption costs from power outages in China," Energy Economics, Elsevier, vol. 105(C).
    20. Satoshi Tsuchiya & Hirokazu Tatano & Norio Okada, 2007. "Economic Loss Assessment due to Railroad and Highway Disruptions," Economic Systems Research, Taylor & Francis Journals, vol. 19(2), pages 147-162.
    21. Wenping Xu & Zongjun Wang & Liu Hong & Ligang He & Xueguang Chen, 2015. "The uncertainty recovery analysis for interdependent infrastructure systems using the dynamic inoperability input–output model," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(7), pages 1299-1306, May.
    22. Adam Rose, 2004. "Economic Principles, Issues, and Research Priorities in Hazard Loss Estimation," Advances in Spatial Science, in: Yasuhide Okuyama & Stephanie E. Chang (ed.), Modeling Spatial and Economic Impacts of Disasters, chapter 2, pages 13-36, Springer.
    23. Chenyang Lian & Yacov Y. Haimes, 2006. "Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input–output model," Systems Engineering, John Wiley & Sons, vol. 9(3), pages 241-258, September.
    24. Barker, Kash & Santos, Joost R., 2010. "Measuring the efficacy of inventory with a dynamic input-output model," International Journal of Production Economics, Elsevier, vol. 126(1), pages 130-143, July.
    25. Yasuhide Okuyama, 2007. "Economic Modeling for Disaster Impact Analysis: Past, Present, and Future," Economic Systems Research, Taylor & Francis Journals, vol. 19(2), pages 115-124.
    26. Rahmatallah Poudineh and Tooraj Jamasb, 2017. "Electricity Supply Interruptions: Sectoral Interdependencies and the Cost of Energy Not Served for the Scottish Economy," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
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