On input-Output economic models in disaster impact assessment

stance in some of the already mentioned models such as ARIO. Moreover, the combination of I/O models and econometric models has been considered in the literature [208,209]. Instances in- clude INFORUM models [210] and FIDELIO 2, a fully interregional dynamic econometric long-term I/O model for the EU and beyond [211]. In some cases, analysis frameworks have also been constructed by involving multiple techniques among those reviewed above in this section. Such is the case of the multistep procedure proposed in Ref. [212], which addresses direct loss assessment, economic shock, pre- recovery period, recovery period and total consequences. The method contemplates the exploitation of the basic equation and of the ARIO model at specific analysis stages. A generalized dynamic I/O framework is proposed in Ref. [213] by combining intertemporal dynamic mod- eling principles with the intratemporal representation of production L. Galbusera, G. Giannopoulos International Journal of Disaster Risk Reduction 30 (2018) 186–198 193 and market clearing. The approach allows to consider both demand and supply constraints and has a strong nexus with static and dynamic Leontief models as well as with SIMs. Another interesting trend in dynamic analysis is that of merging the advantages of the I/O-based economic representation with that of heterogeneous modeling components, able to track more specifically dynamical features of systems involved in the disaster scenarios under consideration. Examples of matchings with technological models can be found for instance in Ref. [214] (transportation network) and [215] (electrical transmission grid). Other recent references include [212], integrating of the I/O framework with a biophysical model for flood risk assessment, and [216], proposing a combined I/O technique and system dynamics ecological model. 5. I/O models and resilience assessment Resilience in economic systems is a central topic in today's research in disaster impacts assessment and mitigation. Aspects of great interest include the definition and measurement of this property [217] as well as the associated policy implications [218]. Recently, a number of characterizations of economic resilience relevant to multi-industry re- presentations have been provided in [219,220]. The latter reference, in particular, qualifies this notion as focusing more on flow losses than on stock losses. Additionally, it defines static economic resilience as “the ability of a system to maintain function when shocked”, while dynamic economic resilience in terms of “hastening the speed of recovery from a shock”. Moreover, it is possible to distinguish between inherent (built- in) and adaptive (arising out of ingenuity under stress) aspects of a resilient economic behavior, as well as between final customer-side and business-side resilience measures. In turn, the latter ones can be broken up as follows, taking into account the double nature of businesses as customers of intermediate goods and suppliers: • customer-side measures: these represent ways for the different in- dustries to effectively exploit available input resources in order to minimize impacts on their own activity; • supplier-side measures: here the focus is on the ability of businesses to keep delivering service. For both cases, the reference discusses a categorized series of resilience options. Micro-, meso- and macroeconomic levels are taken into ac- count in this study. Finally, resilience indicators and indexes from the literature are assessed. Traditional approaches to impact analysis based on I/O models are challenged in providing resilience-oriented interpretations of economic systems and applications, as resilience “places greater emphasis on flex- ibility and responding effectively to the realities of disequilibria, as opposed to unrealistically smooth equilibrium time-paths” [221]. In recent years, remarkable efforts have been made by the scientific community in the use of I/O techniques to address various aspects of resilience analysis. In the first place, I/O structures are being studied in the literature as possible determinants of shock response and resilience attributes of economies. This topic is inherent, for instance, to a number of works on structural analysis and on network-theoretical methods. Moreover, empirical validation has been performed in recent works, especially in a regional perspective. For instance, I/O methods are employed in Ref. [222] to assess regional labor market resilience. Adopting an evolu- tionary approach, the two phases of shock and recovery are considered. Key factors of regional resilience are identified in embeddedness, re- latedness and connectivity, where the first reflects the dependency of shock propagation on the I/O structure of the region, while the other two are associated to intersectoral and interregional labor mobility. Another case can be found in Ref. [223], combining I/O modeling and shift-share analysis to assess regional resilience to economic crisis. Furthermore, resilience concepts, factors and metrics have been integrated in some of the models illustrated above in this paper, especially with reference to some of the dynamic frameworks. For in- stance, as mentioned, the DIIM was complemented in Ref. [199] with the representation of the buffering capabilities provided by inventories, while in [198] the inventory DIIM was also enriched considering dif- ferent types of recovery paths. The DIIM is also studied in Ref. [224] through the concepts of static and dynamic economic resilience and the related resilience triangle representation, see [225]. Attributes such as robustness, rapidity, redundancy and resourcefulness allow the for- mulation of resilience metrics, including the time-averaged level of inoperability, the maximum loss of sector functionality and the time to recovery. A combined demand- and supply-driven I/O analysis frame- work for resilience assessment was introduced in Ref. [107]; in the considered port disruption application, resilience measures were iden- tified in terms of: ship re-routing; export diversion; use of inventories; conservation; unused capacity; input substitution; import substitution; production recapture (rescheduling). A risk management perspective has also been adopted in proposing the exploitation of I/O models for resource allocation and prioritiza- tion. For instance, the IIM has been exploited in Ref. [226] to address preparedness considerations in a multi-regional perspective. Also, in Ref. [200] inventory resources allocation has been considered in the DIIM by means of an optimization technique taking into account in- operability, inventory costs and technical constraints. Resilience me- trics and aspects of the failure and recovery processes reverberate in a number of recent formulations of optimization problems for I/O sys- tems. One such example is [227], wherein an extended Leontief I/O model is embedded into an energy-economic resilience optimization problem. This relates to the determination of “the minimum level of ex- trinsic resource recovery investments required to restore the production levels sufficiently, such that the total economic impacts do not exceed a stipulated level over a stipulated post-disruption duration”. Finally, decision theory has also benefited from the assimilation of I/O techniques and datasets towards the formulation of resilience assessment methods, see for in- stance [228]. 6. Conclusions In Ref. [229], some reflections are proposed on the emerging challenges and opportunities for I/O analysis: exploiting increasing volumes of data available and fostering estimation capabilities; in- tegrating the I/O analysis framework with other techniques; tackling the study of global supply chains, emerging economies and global cities; expanding regional accounting systems; exploiting multipliers; favor supply chain literacy in conjunction with the evolution of Internet of Things; increasing the frequency of I/O tables computation, taking into consideration both the national and inter-country dimensions. In this paper, in particular, we addressed the relationships between I/O modeling and the assessment of economic losses associated to disasters resulting from both natural and man made hazards. The re- levance of I/O models in the context finds a huge plus in their moderate data requirements and their ability to combine with other analysis techniques, such as technological models or market behavioral de- scriptors. In this sense, they could maintain a relevant role in policy support, especially for large scale impact analysis, and in determining a cost-effective use of resources [230]. We documented the recent evolution of the discipline to support a better understanding, measuring and counteracting of complex dis- asters scenarios affecting societies and economies. Theoretical problems and practical case studies explored in research often involve com- plementary views of rippling phenomena, including both backward and forward aspects of propagation. The literature has considerably ex- panded and extended classical demand- and supply-driven I/O for- mulations to take into account the dynamics of critical events and crisis response. The interaction with other disciplines, such as complex net- work theory, also aims at addressing some of the emerging problems, such as the large-scale behavior of interacting economies and supply L. Galbusera, G. Giannopoulos International Journal of Disaster Risk Reduction 30 (2018) 186–198 194 chains. Resilience analysis of economic systems also represents an op- portunity for an evolved approach to I/O modeling, involving a con- tinuous dialog with complementary analysis frameworks. References [1] Y. Okuyama, J.R. Santos, Disaster impact and input-output analysis, Econ. Syst. Res. 26 (1) (2014) 1–12. [2] G. Pescaroli, D. Alexander, A definition of cascading disasters and cascading ef- fects: going beyond the toppling dominos metaphor, Planet@ Risk 3 (1) (2015) 58–67. [3] D. Alexander, A magnitude scale for cascading disasters, International Journal of Disaster Risk Reduction, in press. [4] H. Cochrane, The economics of disaster: retrospect and prospect, Econ. Nat. Unna. Disasters (2010) 65. [5] D.C. Dacy, H. Kunreuther, Economics of Natural Disasters; Implications for Federal Policy, Free Press, New York, 1969. [6] C.T. West, D.G. Lenze, Modeling the regional impact of natural disaster and re- covery: a general framework and an application to Hurricane Andrew, Int. Reg. Sci. Rev. 17 (2) (1994) 121–150. [7] A. Rose, Economic principles, issues, and research priorities in hazard loss esti- mation, in: Y. Okuyama, S.E. Chang (Eds.), Modeling Spatial and Economic Impacts of Disasters, Springer, 2004, pp. 13–36. [8] M.R. Greenberg, M. Lahr, N. Mantell, Understanding the economic costs and benefits of catastrophes and their aftermath: a review and suggestions for the US federal government, Risk Anal. 27 (1) (2007) 83–96. [9] World Bank, United Nations, Natural hazards, unnatural disasters: the economics of effective prevention, The World Bank, 2010. [10] S. Lazzaroni, P.A. van Bergeijk, Natural disasters' impact, factors of resilience and development: a meta-analysis of the macroeconomic literature, Ecol. Econ. 107 (2014) 333–346. [11] S. Kelly, Estimating economic loss from cascading infrastructure failure: a per- spective on modelling interdependency, Infrastruct. Complex. 2 (1) (2015) 7. [12] S. Menoni, C. Bonadonna, M. García-Fernández, R. Schwarze, Recording disaster losses for improving risk modelling capacities, in: K. Poljanšek, M. Marin Ferrer, T. De Groeve, I. Clark (Eds.), Science for disaster risk management 2017: knowing better and losing less, chap. 2.4, EUR 28034 EN, Publications Office of the European Union, Luxembourg, 2017, pp. 83–95. [13] C. Benson, Indirect Economic Impacts from Disasters, review commissioned by Foresight Project: Reducing the Risks of Future Disasters, Government Office for Science, London, 2012. [14] J. Oosterhaven, J. Többen, Wider economic impacts of heavy flooding in Germany: a non-linear programming approach, Spat. Econ. Anal. 12 (4) (2017) 404–428. [15] G.R. West, Comparison of input-output, input-output. econometric and compu- table general equilibrium impact models at the regional level, Econ. Syst. Res. 7 (2) (1995) 209–227. [16] Y. Okuyama, Economic Impacts of Natural Disasters: Development Issues and Empirical Analysis, in: 17th International Input-Output Conference, 2009. [17] E.E. Koks, M. Thissen, A multiregional impact assessment model for disaster analysis, Econ. Syst. Res. 28 (4) (2016) 429–449. [18] J. Oosterhaven, M.C. Bouwmeester, A new approach to modeling the impact of disruptive events, J. Reg. Sci. 56 (4) (2016) 583–595. [19] Y. Kajitani, H. Tatano, Applicability of a spatial computable general equilibrium model to assess the short-term economic impact of natural disasters, Econ. Syst. Res. (2017) 1–24. [20] S. Robinson, Multisectoral models, in: H. Chenery, T. Srinivasan (Eds.), Handbook of Development Economics, 2, chap. 18, Elsevier, 1989, pp. 885–947. [21] A. Rose, Input-output economics and computable general equilibrium models, Struct. Change Econ. Dyn. 6 (3) (1995) 295–304. [22] J. Li, D. Crawford-Brown, M. Syddall, D. Guan, Modeling imbalanced economic recovery following a natural disaster using input-output analysis, Risk Anal. 33 (10) (2013) 1908–1923. [23] M. Ouyang, Review on modeling and simulation of interdependent critical infra- structure systems, Reliab. Eng. Syst. Saf. 121 (2014) 43–60. [24] E. Koks, L. Carrera, O. Jonkeren, J. Aerts, T. Husby, M. Thissen, G. Standardi, J. Mysiak, Regional disaster impact analysis: comparing input-output and com- putable general equilibrium models, Nat. Hazards Earth Syst. Sci. Discuss 3 (2015) 7053–7088. [25] R.E. Miller, P.D. Blair, Input-output Analysis: Foundations and Extensions, Cambridge University Press, 2009. [26] A. Rose, Analyzing terrorist threats to the economy: a computable general equi- librium approach, in: H.W. Richardson, P. Gordon, J.E. Moore II (Eds.), The Economic Impacts of Terrorist Attacks, chap. 11, Edward Elgar Publishing, 2007a, pp. 196–217. [27] Y. Okuyama, Disaster and economic structural change: case study on the 1995 Kobe earthquake, Econ. Syst. Res. 26 (1) (2014) 98–117. [28] J.M. Rueda-Cantuche, The construction of input–output coefficients, in: T. ten Raa (Ed.), Handbook of Input–Output Analysis, chap. 4, Edward Elgar Publishing, 2017, pp.133–174. [29] J.M. Gould, Input/output Databases: Uses in Business and Government, Routledge, 2018. [30] R. Stone, Input-output and national accounts, Organ. Eur. Econ. Coop. (1961). [31] T. ten Raa, Input-output Economics: Theory and Applications: Featuring Asian Economies, World Scientific, 2010. [32] J.W. Kendrick, The new system of national accounts, vol. 47 of Recent Economic Thought, Springer Science & Business Media, 2012. [33] M. Lenzen, K. Kanemoto, D. Moran, A. Geschke, Mapping the structure of the world economy, Environ. Sci. Technol. 46 (15) (2012) 8374–8381. [34] M. Lenzen, D. Moran, K. Kanemoto, A. Geschke, Building Eora: a global multi- region input-output database at high country and sector resolution, Econ. Syst. Res. 25 (1) (2013) 20–49. [35] Eurostat, Eurostat Manual of Supply, Use and Input-Output Tables, Tech. Rep., Eurostat Methodologies and Working Papers, 2008. [36] A. Tukker, A. de Koning, R. Wood, T. Hawkins, S. Lutter, J. Acosta, J.M. Rueda Cantuche, M. Bouwmeester, J. Oosterhaven, T. Drosdowski, J. Kuenen, EXIOPOL- development and illustrative analyses of a detailed global MR EE SUT/IOT, Econ. Syst. Res. 25 (1) (2013) 50–70. [37] R. Wood, K. Stadler, T. Bulavskaya, S. Lutter, S. Giljum, A. de Koning, J. Kuenen, H. Schütz, J. Acosta-Fernández, A. Usubiaga, et al., Global sustainability ac- counting–developing EXIOBASE for multi-regional footprint analysis, Sustainability 7 (1) (2014) 138–163. [38] B. Meng, Y. Zhang, S. Inomata, Compilation and applications of IDE-JETRO's in- ternational input-output tables, Econ. Syst. Res. 25 (1) (2013) 122–142. [39] D.W. Eisenmenger, H. Schandl, Working Party on Environmental Information, Tech. Rep. ENV/EPOC/WPEI(2017)1, OECD, 2017. [40] N. Yamano, N. Ahmad, The OECD input-output database, OECD publishing, 2006. [41] United States Bureau of Economic Analysis, The Detailed Input-output Structure of the US Economy, 1977: Total requirements for commodities and industries, vol. 2, US Department of Commerce, Bureau of Economic Analysis, 1984. [42] M. Timmer, A.A. Erumban, R. Gouma, B. Los, U. Temurshoev, G.J. de Vries, I.-a. Arto, V.A.A. Genty, F. Neuwahl, J. Francois, et al., The world input-output data- base (WIOD): contents, sources and methods, Tech. Rep., Institute for International and Development Economics, 2012. [43] E. Dietzenbacher, B. Los, R. Stehrer, M. Timmer, G. De Vries, The construction of world input-output tables in the WIOD project, Econ. Syst. Res. 25 (1) (2013) 71–98. [44] W. Leontief, Environmental repercussions and the economic structure: an input- output approach, Rev. Econ. Stat. 52 (3) (1970) 262–271. [45] R. Hoekstra, J.C. van den Bergh, Constructing physical input-output tables for environmental modeling and accounting: framework and illustrations, Ecol. Econ. 59 (3) (2006) 375–393. [46] E. Dietzenbacher, S. Giljum, K. Hubacek, S. Suh, Physical input-output analysis and disposals to nature, in: S. Suh (Ed.), Handbook of Input-Output Economics in Industrial Ecology, Springer, 2009, pp. 123–137. [47] S. Suh, Handbook of input-output economics in industrial ecology, 23, Springer Science & Business Media, 2009. [48] D. Guha-Sapir, P. Hoyois, P. Wallemacq, R. Below, Annual disaster statistical re- view 2016 - The numbers and trends, Tech. Rep., Centre for Research on the Epidemiology of Disasters (CRED), 2017. [49] Y. Okuyama, Economic modeling for disaster impact analysis: past, present, and future, Econ. Syst. Res. 19 (2) (2007) 115–124. [50] W.W. Leontief, Output, employment, consumption, and investment, Q. J. Econ. 58 (2) (1944) 290–314. [51] H. Nikaido, Convex Structures and Economic Theory, Elsevier, 2016. [52] A. Ghosh, Input-output approach in an allocation system, Economica 25 (97) (1958) 58–64. [53] J. Schumann, On some basic issues of input-output economics: technical structure, prices, imputations, structural decomposition, applied general equilibrium, Econ. Syst. Res. 2 (3) (1990) 229–239. [54] F. Aroche Reyes, M.A. Marquez Mendoza, The Demand Driven and the Supply- Sided Input-Output Models. Notes for the debate, Tech. Rep., University Library of Munich, Germany, 2014. [55] E. Dietzenbacher, In vindication of the Ghosh model: a reinterpretation as a price model, J. Reg. Sci. 37 (4) (1997) 629–651. [56] C.-Y. Chen, A. Rose, The absolute and relative joint stability of input-output production and allocation coefficients, Advances in Input-Output Analysis. Oxford University Press, New York, 1991, pp. 25–36. [57] A. Ghosh, Experiments with Input-output Models: An Application to the Economy of the United Kingdom, Cambridge University Press, 1964. [58] J. Oosterhaven, On the plausibility of the supply-driven input-output model, J. Reg. Sci. 28 (2) (1988) 203–217. [59] G.W. Gruver, On the plausibility of the supply-driven input-output model: a the- oretical basis for input-coefficient change, J. Reg. Sci. 29 (3) (1989) 441–450. [60] J. Oosterhaven, The supply-driven input-output model: a new interpretation but still implausible, J. Reg. Sci. 29 (3) (1989) 459–465. [61] J.Y. Park, The Supply-driven Input-output Model: a Reinterpretation and Extension, in: 19th International Input-Output Conference, 2011. [62] L.De Mesnard, Is the Ghosh model interesting? J. Reg. Sci. 49 (2) (2009) 361–372. [63] U. Temurshoev, Hypothetical extraction and fields of influence approaches: in- tegration and policy implications, eRC Working Paper Series 09/06e, EERC Research Network, Russia and CIS, 2009. [64] A.-I. Guerra, F. Sancho, A Comparison of Input-Output Models: Ghosh Reduces To Leontief (But ’Closing’ Ghosh Makes It More Plausible), Working Papers 450, Barcelona Graduate School of Economics, 2010a. [65] A.-I. Guerra, F. Sancho, Revisiting the original Ghosh model: can it be made more plausible? Econ. Syst. Res. 23 (3) (2011) 319–328. [66] W. Leontief, Input-output Economics, Oxford University Press, 1986. [67] Y. Okuyama, Dynamic input–output analysis, in: T. ten Raa (Ed.), Handbook of Input–Output Analysis, chap. 13, Edward Elgar Publishing, 2017a, pp. 464–484. [68] I. Sandberg, A nonlinear input-output model of a multisectored economy, Écon.: J. L. Galbusera, G. Giannopoulos International Journal of Disaster Risk Reduction 30 (2018) 186–198 195 Econom. Soc. (1973) 1167–1182. [69] M. Chien, L. Chan, Nonlinear input-output model with piecewise affine coeffi- cients, J. Econ. Theory 21 (3) (1979) 389–410. [70] P. Michaelides, A. Belegri-Roboli, M. Markaki, A non-linear Leontief-type input- output model, Tech. Rep., University Library of Munich, Germany, 2012. [71] A. Goicoechea, D.R. Hansen, An input-output model with stochastic parameters for economic analysis, AIIE Trans. 10 (3) (1978) 285–291. [72] A.A. Ebiefung, M.M. Kostreva, The generalized Leontief input-output model and its application to the choice of new technology, Ann. Oper. Res. 44 (2) (1993) 161–172. [73] A. Lang, A. Dantas, Analysing Impacts of Fuel Constraints on Freight Transport and Economy of New Zealand: an Input-Output Analysis. [74] Y. Okuyama, Critical Review of Methodologies on Disaster Impacts Estimation, Background Paper for EDRR Report. [75] J. Oosterhaven, K.R. Polenske, Modern regional input–output and impact analyses, in: R. Capello, P.E. Nijkamp (Eds.), Handbook of regional growth and development theories, chap. 21, Edward Elgar Publishing, 2009, pp. 423–439. [76] E. Cavallo, I. Noy, The Economics of Natural Disasters: A Survey, Tech. Rep., Inter- American Development Bank, Research Department, 2009. [77] J. Oosterhaven, On the limited usability of the inoperability IO model, Econ. Syst. Res. (2017) 1–10. [78] J.-P. Benassy, The Economics of Market Disequilibrium, 6 Academic Press, New York, 1982. [79] A.E. Steenge, M. Bočkarjova, Thinking about imbalances in post-catastrophe economies: an input-output based proposition, Econ. Syst. Res. 19 (2) (2007) 205–223. [80] J.-M. Albala-Bertrand, Political Economy of Large Natural Disasters: With Special Reference to Developing Countries, OUP Catalogue, Oxford University Press, 1993. [81] Y. Okuyama, Economics of natural disasters: a critical review, Res. Pap. 12 (2003) 20–22. [82] S. Hallegatte, An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina, Risk Anal. 28 (3) (2008) 779–799. [83] I. Noy, W. duPont IV, The long-term consequences of natural disasters - A sum- mary of the literature, Work. Pap. Econ. Financ. Sch. Econ. Financ. Vic. Bus. Sch. (2016). [84] H. Toya, M. Skidmore, Economic development and the impacts of natural dis- asters, Econ. Lett. 94 (1) (2007) 20–25. [85] I. Noy, The macroeconomic consequences of disasters, J. Dev. Econ. 88 (2) (2009) 221–231. [86] S.N. Jonkman, M. Bočkarjova, M. Kok, P. Bernardini, Integrated hydrodynamic and economic modelling of flood damage in the Netherlands, Ecol. Econ. 66 (1) (2008) 77–90. [87] S.R. Steinback, Using ready-made regional input-output models to estimate backward-linkage effects of exogenous output shocks, Rev. Reg. Stud. 34 (1) (2004) 57. [88] Y. Okuyama, Globalization and localization of disaster impacts: an empirical ex- amination, in: CESifo Forum, 11, München: ifo Institut für Wirtschaftsforschung an der Universität München, 2010, pp. 56–66. [89] C. Lian, J.R. Santos, Y.Y. Haimes, Extreme risk analysis of interdependent eco- nomic and infrastructure sectors, Risk Anal. 27 (4) (2007) 1053–1064. [90] J.E. Arana, C.J. León, The impact of terrorism on tourism demand, Ann. Tour. Res. 35 (2) (2008) 299–315. [91] A.F.T. Avelino, Disaggregating input-output tables in time: the temporal input- output framework, Econ. Syst. Res. (2017) 1–22. [92] K. Yonemoto, Changes in the input-output structures of the six regions of Fukushima, Japan: 3 years after the disaster, J. Econ. Struct. 5 (1) (2016) 2. [93] H.C. Davis, E.L. Salkin, Alternative approaches to the estimation of economic impacts resulting from supply constraints, Ann. Reg. Sci. 18 (2) (1984) 25–34. [94] C. Kerschner, K. Hubacek, Assessing the suitability of input-output analysis for enhancing our understanding of potential economic effects of peak oil, Energy 34 (3) (2009) 284–290. [95] K.P. Donaghy, N. Balta-Ozkan, G.J. Hewings, Modeling unexpected events in temporally disaggregated econometric input-output models of regional economies, Econ. Syst. Res. 19 (2) (2007) 125–145. [96] Y. Okuyama, G.J. Hewings, M. Sonis, Measuring economic impacts of disasters: interregional input-output analysis using sequential interindustry model, in: Modeling Spatial and Economic Impacts of Disasters, Springer, 2004, pp. 77–101. [97] R.B. Olshansky, L.D. Hopkins, L.A. Johnson, Disaster and recovery: processes compressed in time, Nat. Hazards Rev. 13 (3) (2012) 173–178. [98] D. Batten, D. Martellato, Classical versus modern approaches to interregional input-output analysis, Ann. Reg. Sci. 19 (3) (1985) 1–15. [99] M.L. Lahr, A review of the literature supporting the hybrid approach to con- structing regional input-output models, Econ. Syst. Res. 5 (3) (1993) 277–293. [100] T. Wiedmann, H.C. Wilting, M. Lenzen, S. Lutter, V. Palm, Quo Vadis MRIO? Methodological, data and institutional requirements for multi-region input-output analysis, Ecol. Econ. 70 (11) (2011) 1937–1945. [101] W. Isard, I.J. Azis, M.P. Drennan, R.E. Miller, S. Saltzman, E. Thorbecke, Methods of Interregional and Regional Analysis, Taylor & Francis, 2017. [102] C.H. Sawyer, R.E. Miller, Experiments in regionalization of a national input-output table, Environ. Plan. A 15 (11) (1983) 1501–1520. [103] M.L. Lahr, A strategy for producing hybrid regional input-output tables, in: M. Lahr, E. Dietzenbacher (Eds.), Input–Output Analysis: Frontiers and Extensions, Palgrave, 2001. [104] M.L. Lahr, B.H. Stevens, A study of the role of regionalization in the generation of aggregation error in regional input-output models, J. Reg. Sci. 42 (3) (2002) 477–507. [105] R. Bon, Comparative stability analysis of multiregional input-output models: column, row, and Leontief-Strout gravity coefficient models, Q. J. Econ. 99 (4) (1984) 791–815. [106] Y. Okuyama, Disaster and Regional Research, in: Regional Research Frontiers-Vol. 1: Innovations, Regional Growth and Migration, Springer, 2017b, pp. 265–275. [107] A. Rose, D. Wei, Estimating the economic consequences of a port shutdown: the special role of resilience, Econ. Syst. Res. 25 (2) (2013) 212–232. [108] Y. Okuyama, M. Sonis, G.J. Hewings, Economic impacts of an unscheduled, dis- ruptive event: a Miyazawa multiplier analysis, in: Understanding and interpreting economic structure, Springer, 1999, pp. 113–143. [109] S. Hallegatte, V. Przyluski, The economics of natural disasters: concepts and methods, World Bank Policy Research Working Paper 5507, 2010. [110] M. Jahn, Economics of extreme weather events in cities: Terminology and regional impact models, Tech. Rep., Hamburg Institute of International Economics (HWWI) Research Paper 143, 2013. [111] Red Cross, World Disasters Report 2010 - Focus on Urban Risk, International Federation of Red Cross and Red Crescent Societies, Geneva, 2010. [112] Y. Okuyama, Modeling spatial economic impacts of an earthquake: input-output approaches, Disaster Prev. Manag.: Int. J. 13 (4) (2004) 297–306. [113] Y. Okuyama, How shaky was the regional economy after the 1995 Kobe earth- quake? A multiplicative decomposition analysis of disaster impact, Ann. Reg. Sci. 55 (2–3) (2015) 289–312. [114] J.R. Santos, Y.Y. Haimes, Modeling the demand reduction Input-Output (I-O) in- operability due to terrorism of interconnected infrastructures, Risk Anal. 24 (6) (2004) 1437–1451. [115] J.R. Santos, Inoperability input-output modeling of disruptions to interdependent economic systems, Syst. Eng. 9 (1) (2006) 20–34. [116] Y.Y. Haimes, B.M. Horowitz, J.H. Lambert, J.R. Santos, C. Lian, K.G. Crowther, Inoperability input-output model for interdependent infrastructure sectors. I: theory and methodology, J. Infrastruct. Syst. 11 (2) (2005) 67–79. [117] Y.Y. Haimes, B.M. Horowitz, J.H. Lambert, J. Santos, K. Crowther, C. Lian, Inoperability input-output model for interdependent infrastructure sectors. II: case studies, J. Infrastruct. Syst. 11 (2) (2005) 80–92. [118] C.W. Anderson, J.R. Santos, Y.Y. Haimes, A risk-based input-output methodology for measuring the effects of the August 2003 Northeast blackout, Econ. Syst. Res. 19 (2) (2007) 183–204. [119] T. Nyein, Measuring the region-wide impact of tsunami disaster on output and income distribution, Master’s thesis, School of Public Policy and Management, Korea Development Institute, 2010. [120] K.G. Crowther, Y.Y. Haimes, G. Taub, Systemic valuation of strategic preparedness through application of the inoperability input-output model with lessons learned from Hurricane Katrina, Risk Anal. 27 (5) (2007) 1345–1364. [121] J.Y. Park, Application of a Price-Sensitive Supply-Side Input-Output Model to an Examination of the Economic Impacts: The Hurricane Katrina and Rita Disruptions of the US Oil-Industry, in: 2009 Upstate New York of Society Chapter for Risk Analysis Symposium. [122] J. Wu, N. Li, S. Hallegatte, P. Shi, A. Hu, X. Liu, Regional indirect economic impact evaluation of the 2008 Wenchuan Earthquake, Environ. Earth Sci. 65 (1) (2012) 161–172. [123] J.R. Santos, L. May, A.E. Haimar, Risk-based input-output analysis of influenza epidemic consequences on interdependent workforce sectors, Risk Anal. 33 (9) (2013) 1620–1635. [124] K.D.S. Yu, R.R. Tan, J.R. Santos, Impact estimation of flooding in Manila: An in- operability input-output approach, in: 2013 IEEE Systems and Information Engineering Design Symposium (SIEDS), IEEE, 2013, pp. 47–51. [125] O. Banerjee, M. Cicowiez, S. Gachot, A quantitative framework for assessing public investment in tourism - An application to Haiti, Tour. Manag. 51 (2015) 157–173. [126] A. Laugé, J. Hernantes, J.M. Sarriegi, The role of critical infrastructures' inter- dependencies on the impacts caused by natural disasters, in: International Workshop on Critical Information Infrastructures Security, Springer, 2013, pp. 50–61. [127] C.A. MacKenzie, J.R. Santos, K. Barker, Measuring changes in international pro- duction from a disruption: case study of the Japanese earthquake and tsunami, Int. J. Prod. Econ. 138 (2) (2012) 293–302. [128] I. Arto, V. Andreoni, J.M. Rueda-Cantuche, Worldwide economic tsunami from the 2011 Japanese disaster, in: 22nd International Input-Output Conference, 2014, pp. 14–18. [129] I. Arto, V. Andreoni, J.M. Rueda Cantuche, Global impacts of the automotive supply chain disruption following the Japanese earthquake of 2011, Econ. Syst. Res. 27 (3) (2015) 306–323. [130] C. Boehm, A. Flaaen, N. Pandalai-Nayar, Input Linkages and the Transmission of Shocks: Firm-Level Evidence from the 2011 Tohōku Earthquake, Tech. Rep., Board of Governors of the Federal Reserve System (US), 2015. [131] V. Carvalho, M. Nirei, Y. Saito, A. Tahbaz-Salehi, Supply Chain Disruptions: Evidence from the Great East Japan Earthquake, Tech. Rep., CEPR Discussion Papers, 2016. [132] M. Kunz, B. Mühr, T. Kunz-Plapp, J. Daniell, B. Khazai, F. Wenzel, M. Vannieuwenhuyse, T. Comes, F. Elmer, K. Schröter, et al., Investigation of superstorm Sandy 2012 in a multi-disciplinary approach, Nat. Hazards Earth Syst. Sci. 13 (10) (2013) 2579. [133] H.W. Richardson, J. Park, J.E. Moore II, Q. Pan, National Economic Impact Analysis of Terrorist Attacks and Natural Disasters, Edward Elgar Publishing, 2014. [134] H.S. in den Bäumen, J.Többen, M. Lenzen, Labour forced impacts and production losses due to the 2013 flood in Germany, Journal of Hydrology, 527, 2015, pp. L. Galbusera, G. Giannopoulos International Journal of Disaster Risk Reduction 30 (2018) 186–198 196 142–150. [135] P.N. Rasmussen Studies in inter-sectoral relations E. Harck 15, 1956. [136] A.-I. Guerra, F. Sancho, Merging the Hypothetical Extraction Method and the Classical Multiplier Approach: A Hybrid Possibility, in: 18th International Input- output Conference, 2010b, pp. 25–28. [137] E. Dietzenbacher, R.E. Miller, Reflections on the inoperability input-output model, Econ. Syst. Res. 27 (4) (2015) 478–486. [138] J.A. Olsen, Input-output models, directed graphs and flows in networks, Econ. Model. 9 (4) (1992) 365–384. [139] J. McNerney, Network properties of economic input-output networks, Tech. Rep., IIASA Interim Report. IIASA, Laxenburg, Austria: IR-09-003, 2009. [140] J. McNerney, B.D. Fath, G. Silverberg, Network structure of inter-industry flows, Phys. A: Stat. Mech. Appl. 392 (24) (2013) 6427–6441. [141] E. Fisher, F. Vega-Redondo, The linchpins of a modern economy, in: AEA Annual Meeting, Chicago, IL, Citeseer, 2006. [142] F. Blöchl, F.J. Theis, F. Vega-Redondo, E.O. Fisher, Vertex centralities in input- output networks reveal the structure of modern economies, Phys. Rev. E 83 (4) (2011) 046127. [143] M. Xu, B.R. Allenby, J.C. Crittenden, Interconnectedness and resilience of the US economy, Adv. Complex Syst. 14 (05) (2011) 649–672. [144] I. Aldasoro, I. Angeloni, Input-output-based measures of systemic importance, Quant. Financ. 15 (4) (2015) 589–606. [145] J. Rodrigues, A. Marques, R. Wood, A. Tukker, A network approach for assembling and linking input-output models, Econ. Syst. Res. 28 (4) (2016) 518–538. [146] M.G.A. Contreras, G. Fagiolo, Propagation of economic shocks in input-output networks: a cross-country analysis, Phys. Rev. E 90 (6) (2014) 062812. [147] W. Li, D.Y. Kenett, K. Yamasaki, H.E. Stanley, S. Havlin, Ranking the economic importance of countries and industries, J. Netw. Theory Financ. 3 (3) (2017) 1–17. [148] M.P. Timmer, A.A. Erumban, B. Los, R. Stehrer, G.J. de Vries, Slicing up global value chains, J. Econ. Perspect. 28 (2) (2014) 99–118. [149] K. Muradov, Determinants of country positioning in global value chains, in: Proceedings of the 25th International Input-Output Conference, 2017. [150] L. Xing, Analysis of inter-country input-output table based on citation network: how to measure the competition and collaboration between industrial sectors on the global value chain, PLoS One 12 (9) (2017) e0184055. [151] P. Antràs, D. Chor, T. Fally, R. Hillberry, Measuring the upstreamness of pro- duction and trade flows, Am. Econ. Rev. 102 (3) (2012) 412–416. [152] P. Antràs, D. Chor, Organizing the global value chain, Econometrica 81 (6) (2013) 2127–2204. [153] R.E. Miller, U. Temurshoev, Output upstreamness and input downstreamness of industries/countries in world production, Int. Reg. Sci. Rev. 40 (5) (2017) 443–475. [154] E. Frohm, V. Gunnella, Sectoral interlinkages in global value chains: spillovers and network effects, Tech. Rep., ECB Working Paper, 2017. [155] D. Acemoglu, A. Ozdaglar, A. Tahbaz-Salehi, Cascades in networks and aggregate volatility, Tech. Rep., National Bureau of Economic Research, 2010. [156] R.E. Lucas Jr, Understanding business cycles, in: Carnegie-Rochester conference series on public policy, 5, Elsevier, 1977, pp. 7–29. [157] D. Acemoglu, V.M. Carvalho, A. Ozdaglar, A. Tahbaz-Salehi, The network origins of aggregate fluctuations, Econometrica 80 (5) (2012) 1977–2016. [158] E. Atalay, How important are sectoral shocks? Am. Econ. J.: Macroecon. 9 (4) (2017) 254–280. [159] K.-O. Vogstad, Input-output analysis and linear programming, in: S. Suh (Ed.), Handbook of Input-output Economics in Industrial Ecology, Springer, 2009, pp. 801–818. [160] G.B. Dantzig, On the status of multistage linear programming problems, Manag. Sci. 6 (1) (1959) 53–72. [161] M. Baghersad, C.W. Zobel, Economic impact of production bottlenecks caused by disasters impacting interdependent industry sectors, Int. J. Prod. Econ. 168 (2015) 71–80. [162] Y.Y. Haimes, P. Jiang, Leontief-based model of risk in complex interconnected infrastructures, J. Infrastruct. Syst. 7 (1) (2001) 1–12. [163] K.G. Crowther, Y.Y. Haimes, Application of the inoperability input-output model (IIM) for systemic risk assessment and management of interdependent infra- structures, Syst. Eng. 8 (4) (2005) 323–341. [164] R. Pant, K. Barker, F.H. Grant, T.L. Landers, Interdependent impacts of inoper- ability at multi-modal transportation container terminals, Transp. Res. Part E: Logist. Transp. Rev. 47 (5) (2011) 722–737. [165] M. Percoco, A note on the inoperability input-output model, Risk Anal. 26 (3) (2006) 589–594. [166] M. Percoco, On the local sensitivity analysis of the inoperability input-output model, Risk Anal. 31 (7) (2011) 1038–1042. [167] J. Jung, J.R. Santos, Y.Y. Haimes, International Trade Inoperability Input-Output Model (IT-IIM): theory and application, Risk Anal. 29 (1) (2009) 137–154. [168] R. Setola, S. De Porcellinis, M. Sforna, Critical infrastructure dependency assess- ment using the input-output inoperability model, Int. J. Crit. Infrastruct. Prot. 2 (4) (2009) 170–178. [169] M. Leung, Y.Y. Haimes, J.R. Santos, Supply- and output-side extensions to the inoperability input-output model for interdependent infrastructures, J. Infrastruct. Syst. 13 (4) (2007) 299–310. [170] L. Ocampo, J.G. Masbad, V.M. Noel, R.S. Omega, Supply-side inoperability input- output model (SIIM) for risk analysis in manufacturing systems, J. Manuf. Syst. 41 (2016) 76–85. [171] P. Jiang, Y.Y. Haimes, Risk management for Leontief-based interdependent sys- tems, Risk Anal. 24 (5) (2004) 1215–1229. [172] B. Gallego, M. Lenzen, A consistent input-output formulation of shared producer and consumer responsibility, Econ. Syst. Res. 17 (4) (2005) 365–391. [173] P.J. Schneider, B.A. Schauer, HAZUS–its development and its future, Nat. Hazards Rev. 7 (2) (2006) 40–44. [174] R.T. Eguchi, H.A. Seligson, Loss estimation models and metrics, in: A. Bostrom, S. P. French, S.J. Gottlieb (Eds.), Risk Assessment, Modeling and Decision Support, Springer, 135–170, 2008, pp. 135–170. [175] H. Cochrane, S. Chang, A. Rose, Indirect economic losses, Development of Standardized Earthquake Loss Estimation Methodology Vol. II. [176] D.S. Brookshire, S.E. Chang, H. Cochrane, R.A. Olson, A. Rose, J. Steenson, Direct and indirect economic losses from earthquake damage, Earthq. Spectra 13 (4) (1997) 683–701. [177] C.A. Kircher, R.V. Whitman, W.T. Holmes, HAZUS earthquake loss estimation methods, Nat. Hazards Rev. 7 (2) (2006) 45–59. [178] C. Scawthorn, P. Flores, N. Blais, H. Seligson, E. Tate, S. Chang, E. Mifflin, W. Thomas, J. Murphy, C. Jones, et al., HAZUS-MH flood loss estimation metho- dology. II. Damage and loss assessment, Nat. Hazard. 7 (2) (2006) 72–81. [179] J. Park, P. Gordon, J.E. Moore II, H.W. Richardson, L. Wang, Simulating the state- by-state effects of terrorist attacks on three major US Ports: Applying NIEMO (National Interstate Economic Model), in: Richardson H.W., Gordon P., Moore II J.E. (Eds.), The economic costs and consequences of terrorism, chap. 11, Edward Elgar Publishing, 2007, pp. 208–234. [180] J. Park, H.W. Richardson, National Interstate Economic Model (NIEMO), in: H.W. Richardson, J. Park, J.E. Moore II, Q. Pan (Eds.), National Economic Impact Analysis of Terrorist Attacks and Natural Disasters, chap. 2, Edward Elgar Publishing, 2014, pp. 4–23. [181] J. Park, J. Cho, P. Gordon, J.E. Moore II, H.W. Richardson, S. Yoon, Adding a freight network to a national interstate input-output model: a TransNIEMO ap- plication for California, J. Transp. Geogr. 19 (6) (2011) 1410–1422. [182] J. Cho, P. Gordon, J.E. Moore II, Q. Pan, J. Park, H.W. Richardson, TransNIEMO: economic impact analysis using a model of consistent inter-regional economic and network equilibria, Transp. Plan. Technol. 38 (5) (2015) 483–502. [183] J. Park, P. Gordon, H.W. Richardson, Constructing a Flexible National Interstate Economic Model (FlexNIEMO), in: Proceedings of the 19th International Input- Output Conference, pp. 13-17. [184] J. Park, P. Gordon, Y. Kim, J.E. Moore II, H.W. Richardson, The Temporal Regional Economic Impacts of a Hurricane Disaster on Oil Refinery Operations: A FlexNIEMO Approach, in: A. Abbas, M. Tambe, D. Von Winterfeldt (Eds.), Improving Homeland Security Decisions, Cambridge University Press, 2017, pp. 220–237. [185] Y. Okuyama, M. Sonis, G.J. Hewings, Typology of structural change in a regional economy: a temporal inverse analysis, Econ. Syst. Res. 18 (2) (2006) 133–153. [186] R.M. del Río-Chanona, J. Grujić, H.J. Jensen, Trends of the world input and output network of global trade, PLoS One 12 (1) (2017) e0170817. [187] S. Cole, The delayed impacts of plant closures in a reformulated Leontief model, Pap. Reg. Sci. 65 (1) (1988) 135–149. [188] S. Cole, Expenditure lags in impact analysis, Reg. Stud. 23 (2) (1989) 105–116. [189] E. Romanoff, S.H. Levine, Interregional sequential interindustry modeling: a pre- liminary analysis of regional growth and decline in a two region case, Northeast Reg. Sci. Rev. 7 (1) (1977) 87–101. [190] J.K. Sharp, W.R. Perkins, A new approach to dynamic input-output models, Automatica 14 (1) (1978) 77–79. [191] R.E. Quandt, Econometric disequilibrium models, Econom. Rev. 1 (1) (1982) 1–63. [192] A. Rose, G. Oladosu, S.-Y. Liao, Regional economic impacts of a terrorist attack on the water system of Los Angeles: a computable general disequilibrium analysis, in: H.W. Richardson, P. Gordon, J.E. Moore II (Eds.), The Economic Costs and Consequences of Terrorism, chap. 15, Edward Elgar Publishing, 2007, pp. 291–316. [193] M. Bočkarjova, A.E. Steenge, A. van der Veen, On direct estimation of initial da- mage in the case of a major catastrophe: derivation of the basic equation, Disaster Prev. Manag.: Int. J. 13 (4) (2004) 330–336. [194] C. Lian, Y.Y. Haimes, Managing the risk of terrorism to interdependent infra- structure systems through the dynamic inoperability input-output model, Syst. Eng. 9 (3) (2006) 241–258. [195] E. Kujawski, Multi-period model for disruptive events in interdependent systems, Syst. Eng. 9 (4) (2006) 281–295. [196] R. Akhtar, J.R. Santos, Risk-based input-output analysis of hurricane impacts on interdependent regional workforce systems, Nat. Hazards 65 (1) (2013) 391–405. [197] M.J. Orsi, J.R. Santos, Estimating workforce-related economic impact of a pan- demic on the Commonwealth of Virginia, IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 40 (2) (2010) 301–305. [198] O. Jonkeren, G. Giannopoulos, Analysing critical infrastructure failure with a re- silience inoperability input-output model, Econ. Syst. Res. 26 (1) (2014) 39–59. [199] K. Barker, J.R. Santos, Measuring the efficacy of inventory with a dynamic input- output model, Int. J. Prod. Econ. 126 (1) (2010) 130–143. [200] L. Galbusera, I. Azzini, O. Jonkeren, G. Giannopoulos, Inoperability input-output modeling: inventory optimization and resilience estimation during critical events, ASCE-ASME J. Risk Uncertain. Eng. Syst. Part A: Civil. Eng. 2 (3) (2016) B4016001. [201] J.Z. Resurreccion, J. Santos, Integrated Stochastic Inventory and Input-Output Models for Enhancing Disaster Preparedness of Disrupted Interdependent Sectors, in: Proceedings of the 20th International Input-Output Conference, 2012. [202] J.R. Santos, K.D.S. Yu, S.A.T. Pagsuyoin, R.R. Tan, Time-varying disaster recovery model for interdependent economic systems using hybrid input-output and event tree analysis, Econ. Syst. Res. 26 (1) (2014) 60–80. [203] A. Niknejad, D. Petrovic, A fuzzy dynamic inoperability input-output model for L. Galbusera, G. Giannopoulos International Journal of Disaster Risk Reduction 30 (2018) 186–198 197 strategic risk management in global production networks, Int. J. Prod. Econ. 179 (2016) 44–58. [204] M.J. Orsi, J.R. Santos, Incorporating time-varying perturbations into the dynamic inoperability input-output model, IEEE Trans. Syst. Man Cybern.-Part A: Syst. Hum. 40 (1) (2010) 100–106. [205] W. Xu, L. Hong, L. He, S. Wang, X. Chen, Supply-driven dynamic inoperability input-output price model for interdependent infrastructure systems, J. Infrastruct. Syst. 17 (4) (2011) 151–162. [206] F. Henriet, S. Hallegatte, L. Tabourier, Firm-network characteristics and economic robustness to natural disasters, J. Econ. Dyn. Control 36 (1) (2012) 150–167. [207] S. Hallegatte, Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters, Risk Anal. 34 (1) (2014) 152–167. [208] P.R. Israilevich, G.J. Hewings, M. Sonis, G.R. Schindler, Forecasting structural change with a regional econometric input-output model, J. Reg. Sci. 37 (4) (1997) 565–590. [209] S.J. Rey, The performance of alternative integration strategies for combining re- gional econometric and input-output models, Int. Reg. Sci. Rev. 21 (1) (1998) 1–35. [210] C. Almon, The INFORUM approach to interindustry modeling, Econ. Syst. Res. 3 (1) (1991) 1–8. [211] K. Kratena, G. Streicher, S. Salotti, M. Sommer, J.M.V. Jaramillo, FIDELIO 2: Overview and theoretical foundations of the second version of the Fully Interregional Dynamic Econometric Long-term Input-Output model for the EU-27, JRC Tech. Rep. 105900, EUR 28503 EN, 2017. [212] E.E. Koks, M. Bočkarjova, H.d. Moel, J.C. Aerts, Integrated direct and indirect flood risk modeling: development and sensitivity analysis, Risk Anal. 35 (5) (2015) 882–900. [213] A.F.T. Avelino, G.J. Hewings, The Challenge of Estimating the Impact of Disasters: many approaches, many limitations and a compromise, Tech. Rep., University of Illinois at Urbana-Champaign, REAL Discussion Papers: REAL 17-T-1, 2017. [214] S. Cho, P. Gordon, I. Moore, E. James, H.W. Richardson, M. Shinozuka, S. Chang, Integrating transportation network and regional economic models to estimate the costs of a large urban earthquake, J. Reg. Sci. 41 (1) (2001) 39–65. [215] O. Jonkeren, I. Azzini, L. Galbusera, S. Ntalampiras, G. Giannopoulos, Analysis of critical infrastructure network failure in the European Union: a combined systems engineering and economic model, Netw. Spat. Econ. 15 (2) (2015) 253–270. [216] M. Cordier, T. Uehara, J. Weih, B. Hamaide, An input-output economic model integrated within a system dynamics ecological model: feedback loop metho- dology applied to fish nursery restoration, Ecol. Econ. 140 (2017) 46–57. [217] S. Hallegatte, Economic resilience: definition and measurement, World Bank Policy Research Working Paper 6852, 2014b. [218] R. Duval, J. Elmeskov, L. Vogel, Structural Policies and Economic Resilience to Shocks, OECD Working Papers 567, 2007. [219] A. Rose, Economic resilience to natural and man-made disasters: multidisciplinary origins and contextual dimensions, Environ. Hazards 7 (4) (2007) 383–398. [220] A. Rose, E. Krausmann, An economic framework for the development of a resi- lience index for business recovery, Int. J. Disaster Risk Reduct. 5 (2013) 73–83. [221] A. Rose, Defining and Measuring Economic Resilience from a Societal, Environmental and Security Perspective, Integrated Disaster Risk Management, Springer, 2017. [222] D. Diodato, A.B. Weterings, The resilience of regional labour markets to economic shocks: exploring the role of interactions among firms and workers, J. Econ. Geogr. 15 (4) (2014) 723–742. [223] E. Giannakis, A. Bruggeman, Economic crisis and regional resilience: evidence from Greece, Pap. Reg. Sci. 96 (3) (2017) 451–476. [224] R. Pant, K. Barker, C.W. Zobel, Static and dynamic metrics of economic resilience for interdependent infrastructure and industry sectors, Reliab. Eng. Syst. Saf. 125 (2014) 92–102. [225] M. Bruneau, S.E. Chang, R.T. Eguchi, G.C. Lee, T.D. O'Rourke, A.M. Reinhorn, M. Shinozuka, K. Tierney, W.A. Wallace, D.Von Winterfeldt, A framework to quantitatively assess and enhance the seismic resilience of communities, Earthq. Spectra 19 (4) (2003) 733–752. [226] K.G. Crowther, Y.Y. Haimes, Development of the multiregional inoperability input-output model (MRIIM) for spatial explicitness in preparedness of inter- dependent regions, Syst. Eng. 13 (1) (2010) 28–46. [227] P. He, T.S. Ng, B. Su, Energy-economic recovery resilience with Input-Output linear programming models, Energy Econ. 68 (2017) 177–191. [228] A. Kelic, Z.A. Collier, C. Brown, W.E. Beyeler, A.V. Outkin, V.N. Vargas, M.A. Ehlen, C. Judson, A. Zaidi, B. Leung, et al., Decision framework for evalu- ating the macroeconomic risks and policy impacts of cyber attacks, Environ. Syst. Decis. 33 (4) (2013) 544–560. [229] E. Dietzenbacher, M. Lenzen, B. Los, D. Guan, M.L. Lahr, F. Sancho, S. Suh, C. Yang, Input-output analysis: the next 25 years, Econ. Syst. Res. 25 (4) (2013) 369–389. [230] N. Dormady, A. Rose, H. Rosoff, A. Roa-Henriquez, Estimating the Cost- Effectiveness of Resilience to Disasters: Survey Instrument Design & Refinement of Primary Data, in: M. Ruth, S. Reisemann (Eds.), Handbook on Resilience of Socio- Technical Systems, Edward Elgar, 2017. L. Galbusera, G. Giannopoulos International Journal of Disaster Risk Reduction 30 (2018) 186–198 198