Abstract
Over the past few years, the European Commission has placed Critical Infrastructure Protection under the spotlight. Therefore, the Joint Research Centre is developing a tool to estimate the economic impact of Critical Infrastructure (CI) network failure, resulting from a hazard, on the regional or national level. This tool, which is presented in this study, is a combined Systems Engineering and Dynamic Inoperability Input–output model (SE-DIIM). The resilience of infrastructures and economic sectors, in terms of their ability to withstand and recover from disruptions, is included in the model. We discuss the model by analyzing the economic losses incurred in the 2003 Italian electricity network outage. The losses are estimated at both national and regional levels i.e. northern, central and southern parts of Italy and Sicily with a focus on 9 CI’s. We estimate that the economic loss for the case study under consideration is between €46 million and €173 million. We conclude that the combination of the SE and the DIIM components provides a complete framework for assessing the economic impact of critical infrastructure network failure on the national or regional level taking account of resilience.
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Notes
More details on the Directive can be found in http://eur-lex.europa.eu/LexUriServ/LexUriServ.do?uri=OJ:L:2008:345:0075:0082:EN:PDF
The challenge in improving the recovery speed of an infrastructure after a disaster is to maximize infrastructure performance within budgetary restrictions. This is actually a study in itself, as addressed by Matisziw et al. (2010).
So where the SE component analyses failure and recovery within an infrastructure network in the present study, the DIIM component models the interaction between infrastructures.
WIOD actually considers 35 economic sectors but because we close the model with respect to both the household and the government sector, there are 37 sectors in total.
The perturbation in the electricity sector (p elec = 1) is transformed into a perturbation for the aggregated ‘electricity, gas and water supply’ sector as considered in the national Input–output tables from the WIOD on the basis of Structural Business Statistics data from Eurostat. This data source tells us that in Italy the proportion of the production value of the electricity sector is 77,6 % of the production value of the aggregated sector ‘electricity, gas and water supply’.
Full recovery is set equal to p i (T i ) = 0,01 because one cannot divide by zero in Eq. 7.
In our study. this is similar to assuming that inoperability in all industrial sectors, the household sector and the government sector is equal to 1 during the failure period.
Inventories or network redundancies for example.
References
Abrell J, Weigt H (2012) Combining energy networks. Netw and Spat Econ 12:377–401
Ali J, Santos JR (2012) Framework for Evaluating Economic Impact of IT based Disasters on the Interdependent Sectors of the US Economy. Proceedings of the 2012 I.E. Systems and Information Engineering Design Symposium, University of Virginia, Charlottesville, USA, April 27, 2012
Anderson CW, Santos JR, Haimes YY (2007) A risk-based input–output methodology for measuring the effects of the August 2003 northeast blackout. Econ Syst Res 19(2):183–204
Barker K, Santos JR (2010) Measuring the efficacy of inventory with a dynamic input–output model. Int J Prod Econ 126:130–143
Berizzi A (2004) The Italian 2003 blackout. Power Engineering Society General Meeting 1673–1679
Bird BR, Stewart WE, Lightfoot EN (2002) Transport Phenomena. John Wiley & Sons
Buldyrev SV, Parshani R, Paul P, Stanley HE, Havlin S (2010) Catastrophic cascade of failures in interdependent networks. Nature 464:1025–1028
Crowther KG, Haimes YY (2005) Application of the inoperability input–output model (IIM) for systemic risk assessment and management of interdependent infrastructures. Syst Eng 8(4):323–341
Crowther KG, Haimes YY (2010) Development of the multiregional inoperability input–output model (MRIIM) for spatial explicitness in preparedness of interdependent regions. Syst Eng 13(1):28–46
De Nooij M, Koopmans C, Bijvoet C (2007) The value of supply security: the cost of power interruptions: economic input for damage reduction and investment in networks. Energy Econ 29:277–295
Deckers SLJ, Jepsen ON (2003) Conditions monitoring and failure diagnosis in plants of the metals industry. In: Fault Detection, Supervision and Safety of Technical Processes 2003. A Proceedings Volume from the 5th IFAC Symposium, Washington D.C., USA 9–11 June 2003
Devogelaer D, Gusbin D (2004) Een kink in de kabel: de kosten van een storing in de stroomvoorziening. Federaal Planbureau, Brussel
Di Mauro C, Bouchon S, Logtmeijer C, Pride RD, Hartung T, Nordvik JP (2010) A structured approach to identifying European critical infrastructures. Int J of Crit Infrastruct 6(3):277–292
Dietzenbacher E, Los B, Stehrer R, Timmer MP, de Vries GJ (2013) The construction of world input–output tables in the WIOD project. Econ Syst Res 25:71–98
Du L, Peeta S (2014) A stochastic optimization model to reduce expected post-disaster response time through Pre-disaster investment decisions. Netw and Spatial Econ. doi:10.1007/s11067-013-9219-1
Ferrari M, Schupp B, Trucco P, Ward D, Nordvik J (2011) Assessing supply chain dependency on critical infrastructures using fuzzy cognitive maps. Int J of Risk Assess and Manag 15(2–3):149–170
Filippini R, Silva A (2014) A modeling framework for the resilience analysis of networked systems-of-systems based on functional dependencies. Reliab Eng Syst Saf 125:82–91
Gao J, Buldyrev SV, Stanley HE, Havlin S (2012) Networks formed from interdependent networks. Nat Phys 8:40–48
Greenberg M, Haas C, Cox A Jr, Lowrie K, McComas K, North W (2012) Ten most important accomplishments in risk analysis, 1980–2010. Risk Anal 32(5):771–781
GRTN (2004) Rapporto sulle activita’ del gestore della rete di trasmissione nazionale Aprile 2003 – Marzo 2004
Haimes YY, Horowitz BM, Lambert JH, Santos JR, Lian C, Crowther KG (2005a) Inoperability input–output model for interdependent infrastructure sectors. I: theory and methodology. J Infrastruct Syst 11(2):67–79
Haimes YY, Horowitz BM, Lambert JH, Santos JR, Crowther KG, Lian C (2005b) Inoperability input–output model for interdependent infrastructure sectors, II: case studies. J Infrastruct Syst 11(2):80–92
Leontief WW (1951a) Input–output economics. Sci Am 185:15–21
Leontief WW (1951b) The structure of the american economy, 1919–1939, 2nd edn. Oxford University Press, New York
Lian C, Haimes YY (2006) Managing the risk of terrorism to interdependent infrastructure systems through the dynamic inoperability input–output model. Syst Eng 9(3):241–258
Matisziw TC, Murray AT, Grubesic TH (2010) Strategic network restoration. Netw and Spatial Econ 10(3):345–361
Miller RE, Blair PD (2009) Input–output analysis: foundations and extensions, 2nd edn. Cambridge University Press, Cambridge
Oliva G, Panzieri S, Setola R (2011) Fuzzy dynamic input–output inoperability model. Int J Crit Infrastruct Prot 4:165–175
Reggiani A, De Graaff T, Nijkamp P (2002) Resilience: an evolutionary approach to spatial economic systems. Netw and Spatial Econ 2(2):211–229
Rigole T, Deconinck G (2006) A survey on modeling and simulation of interdependent critical infrastructures. 3rd IEEE Benelux Young Researchers Symposium in Electrical Power Engineering, Paper 44
Rocco CM, Ramirez-Marquez JE, Salazar DE (2012) Some metrics for assessing the vulnerability of complex networks: An application to an electric power system. Advances in Safety, Reliability and Risk Management 2556–2561
Rosato V, Bologna S, Tiriticco F (2007) Topological properties of high-voltage electrical transmission networks. Electr Power Syst Res 77:99–105
Rosato V, Issacharoff L, Gianese G, Bologna S (2011) Influence of the topology on the power flux of the Italian high-voltage electrical network. Available at http://arxiv.org/abs/0909.1664 (accessed on December 2011)
Rose AZ (2007) Economic resilience to natural and man-made disasters: multidisciplinary origins and contextual dimensions. Environ Hazards 7:383–398
Rose AZ (2009) A Framework for Analyzing the Total Economic Impacts of Terrorist Attacks and Natural Disasters. Journal of Homeland Security and Emergency Management 6, 1, art.9
Rose AZ, Lim D (2002) Business interruption losses from natural hazards: conceptual and methodological issues in the case of the Northridge earthquake. Environ Hazards 4:1–14
Santos JR, Haimes YY (2004) Modeling the demand reduction input–output (I-O) inoperability Due to terrorism of interconnected infrastructures. Risk Anal 24(6):1437–1451
Sforna M, Delfanti M (2006) Overview of the events and causes of the 2003 Italian blackout. In Proc. IEEE PSCE’06, Atlanta, Nov. 2006: 301–308
Simonsen I (2005) Diffusion and Networks: from simple models to applications. Presentation at 41th Winter School in Theoretical Physics, Ladek
UCTE (2004) Final report of the Investigation Committee on the 28 September 2003 Blackout in Italy
Wilde WD, Warren MJ (2008) Visualisation of critical infrastructure failure. Proceedings of the 9th Australian Information Warfare and Security Conference, 48–96
Acknowledgments
We would like to thank two anonymous referees for their valuable comments. This work is supported by the Annual Work Programme 2010 and Annual Work Programme 2011 for the specific programme on the “Prevention and fight against crime” which is financed by Directorate General Home Affairs of the European Commission. The authors would like to express their gratitude for this support.
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Jonkeren, O., Azzini, I., Galbusera, L. et al. Analysis of Critical Infrastructure Network Failure in the European Union: A Combined Systems Engineering and Economic Model. Netw Spat Econ 15, 253–270 (2015). https://doi.org/10.1007/s11067-014-9259-1
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DOI: https://doi.org/10.1007/s11067-014-9259-1