Assessing the Regional Economic Ripple Effect of Flood Disasters Based on a Spatial Computable General Equilibrium Model Considering Traffic Disruptions

Abstract

With growing regional economic integration, transportation systems have become critical to regional development and economic vitality but vulnerable to disasters. However, the regional economic ripple effect of a disaster is difficult to quantify accurately, especially considering the cumulated influence of traffic disruptions. This study explored integrating transportation system analysis with economic modeling to capture the regional economic ripple effect. A state-of-the-art spatial computable general equilibrium model is leveraged to simulate the operation of the economic system, and the marginal rate of transport cost is introduced to reflect traffic network damage post-disaster. The model is applied to the 50-year return period flood in 2020 in Hubei Province, China. The results show the following. First, when traffic disruption costs are considered, the total output loss of non-affected areas is 1.81 times than before, and non-negligible losses reach relatively remote zones of the country, such as the Northwest Comprehensive Economic Zone (36% of total ripple effects). Second, traffic disruptions have a significant hindering effect on regional trade activities, especially in the regional intermediate input—about three times more than before. The industries most sensitive to traffic disruptions were transportation, storage, and postal service (5 times), and processing and assembly manufacturing (4.4 times). Third, the longer the distance, the stronger traffic disruptions’ impact on interregional intermediate inputs. Thus, increasing investment in transportation infrastructure significantly contributes to mitigating disaster ripple effects and accelerating the process of industrial recovery in affected areas.

Keywords

Economic ripple effect / Floods / Spatial computable general equilibrium model / Supply chain damage / Traffic disruption

References

[1] Anas A. The cost of congestion and the benefits of congestion pricing: A general equilibrium analysis. Transportation Research Part B: Methodological, 2020, 136: 110-137

[2] Ando A, Meng B. The transport sector and regional price differentials: A spatial CGE model for Chinese provinces. Economic Systems Research, 2009, 21(2): 89-113

[3] Armington PS. A theory of demand for products distinguished by place of production. Staff Papers, 1969, 16(1): 159-178

[4] Bachmann C, Kennedy C, Roorda MJ. Applications of random-utility-based multi-region input-output models of transport and the spatial economy. Transport Reviews, 2014, 34(4): 418-440

[5] Bröcker J, Korzhenevych A, Schürmann C. Assessing spatial equity and efficiency impacts of transport infrastructure projects. Transportation Research Part B: Methodological, 2010, 44(7): 795-811

[6] Candelieri A, Galuzzi BG, Giordani I, Archetti F. Vulnerability of public transportation networks against directed attacks and cascading failures. Public Transport, 2019, 11(1): 27-49

[7] Carrera L, Standardi G, Bosello F, Mysiak J. Assessing direct and indirect economic impacts of a flood event through the integration of spatial and computable general equilibrium modelling. Environmental Modelling & Software, 2015, 63: 109-122

[8] Cui Q, Xie W, Liu Y. Effects of sea level rise on economic development and regional disparity in China. Journal of Cleaner Production, 2018, 176: 1245-1253

[9] Dietzenbacher E, van Burken B, Kondo Y. Hypothetical extractions from a global perspective. Economic Systems Research, 2019, 31(4): 505-519

[10] Enke DL, Tirasirichai C, Luna R. Estimation of earthquake loss due to bridge damage in the St. Louis metropolitan area. II: Indirect losses. Natural Hazards Review, 2008, 9(1): 12-19

[11] Galbusera L, Giannopoulos G. On input-output economic models in disaster impact assessment. International Journal of Disaster Risk Reduction, 2018, 30: 186-198

[12] Haddad, E.A., and G.J. Hewings. 2004. Transportation costs, increasing returns and regional growth: An interregional CGE analysis. Conference paper of the 44th Congress of the European Regional Science Association: “Regions and Fiscal Federalism”, 25–29 August 2004, Porto, Portugal.

[13] Haddad EA, Teixeira E. Economic impacts of natural disasters in megacities: The case of floods in São Paulo, Brazil. Habitat International, 2015, 45: 106-113

[14] Hallegatte S. An adaptive regional input-output model and its application to the assessment of the economic cost of Katrina. Risk Analysis, 2008, 28(3): 779-799

[15] Hallegatte S. Modeling the role of inventories and heterogeneity in the assessment of the economic costs of natural disasters. Risk Analysis, 2014, 34(1): 152-167

[16] Ham H, Kim TJ, Boyce D. Assessment of economic impacts from unexpected events with an interregional commodity flow and multimodal transportation network model. Transportation Research Part A: Policy and Practice, 2005, 39(10): 849-860.

[17] Hansen W, Johansen BG. Regional repercussions of new transport infrastructure investments: An SCGE model analysis of wider economic impacts. Research in Transportation Economics, 2017, 63: 38-49

[18] Horridge M. Wittwer G. The TERM model and its database. Practical policy analysis using TERM, 2012, Dordrecht, Netherlands: Springer 13-35.

[19] Hosoe N, Gasawa K, Hashimoto H. Textbook of computable general equilibrium modeling: Programming and simulations, 2010, New York: Palgrave Macmillan

[20] Jiang, X., Y. Lin, and L. Yang. 2023. A simulation-based approach for assessing regional and industrial flood vulnerability using mixed-MRIO model: A case study of Hubei Province, China. Journal of Environmental Management 339: Article 117845.

[21] Kajitani Y, Tatano H. Applicability of a spatial computable general equilibrium model to assess the short-term economic impact of natural disasters. Economic Systems Research, 2018, 30(3): 289-312

[22] Koike A, Tavasszy L, Sato K, Monma T. Spatial incidence of economic benefit of road-network investments: Case studies under the usual and disaster scenarios. Journal of Infrastructure Systems, 2012, 18(4): 252-260

[23] Koike, A., T. Ueda, and M. Thissen. 2015. Economic damage assessment of a catastrophe shocks to physical and social capital in a spatial CGE analysis. https://www.researchgate.net/publication/255563681_Economic_Damage_Assessment_of_a_Catastrophe_Shocks_to_Physical_and_Social_Capital_in_a_Spatial_CGE_Analysis#:~:text=In%20this%20paper%20we%20assess%20the%20economic%20damage,of%20handling%20both%20effects%20at%20the%20same%20time. Accessed 19 May 2023.

[24] Koks EE, Thissen M. A multiregional impact assessment model for disaster analysis. Economic Systems Research, 2016, 28(4): 429-449

[25] Li, Y., L. Yang, D. Wang, Y. Zhou, W. He, B. Li, Y. Yang, and H. Lv. 2021. Identifying the critical transmission sectors with energy-water nexus pressures in China’s supply chain networks. Journal of Environmental Management 289: Article 112518.

[26] Miller RE, Blair PD. Input-output analysis: Foundations and extensions, 2009, Cambridge, UK: Cambridge University Press

[27] Okuda, K., and A. Kawasaki. 2022. Effects of disaster risk reduction on socio-economic development and poverty reduction. International Journal of Disaster Risk Reduction 80: Article 103241.

[28] Pörtner, H., D.C. Roberts, H. Adams, I. Adelekan, C. Adler, R. Adrian, P. Aldunce, E. Ali, et al. 2022. Technical summary. In Climate change 2022: Impacts, adaptation and vulnerability. Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, ed. H.-O. Pörtner, D.C. Roberts, M. Tignor, E.S. Poloczanska, K. Mintenbeck, A. Alegría, M. Craig, S. Langsdorf, et al., 37–118. Cambridge and New York: Cambridge University Press.

[29] Robson EN, Wijayaratna KP, Dixit VV. A review of computable general equilibrium models for transport and their applications in appraisal. Transportation Research Part A: Policy and Practice, 2018, 116: 31-53.

[30] Rokicki B, Haddad EA, Horridge JM, Stępniak M. Accessibility in the regional CGE framework: The effects of major transport infrastructure investments in Poland. Transportation, 2021, 48(2): 747-772

[31] Rose A. Okuyama Y, Chang SE. Economic principles, issues, and research priorities in hazard loss estimation. Modeling spatial and economic impacts of disasters, advances in spatial science, 2004, Berlin: Springer 13-36.

[32] Rose A, Guha G. Okuyama Y, Chang SE. Computable general equilibrium modeling of electric utility lifeline losses from earthquakes. Modeling spatial and economic impacts of disasters, advances in spatial science, 2004, Berlin: Springer 119-141.

[33] Rose A, Walmsley T, Wei D. Spatial transmission of the economic impacts of COVID-19 through international trade. Letters in Spatial and Resource Sciences, 2021, 14(2): 169-196

[34] Samuelson PA. The transfer problem and transport costs: The terms of trade when impediments are absent. The Economic Journal, 1952, 62(246): 278-304

[35] Shahrokhi Shahraki H, Bachmann C. Designing computable general equilibrium models for transportation applications. Transport Reviews, 2018, 38(6): 737-764

[36] Shibusawa, H. 2020. A dynamic spatial CGE approach to assess economic effects of a large earthquake in China. Progress in Disaster Science 6: Article 100081.

[37] Tan, L., X. Wu, Z. Xu, and L. Li. 2019. Comprehensive economic loss assessment of disaster based on CGE model and IO model – A case study on Beijing “7.21 Rainstorm.” International Journal of Disaster Risk Reduction 39: Article 101246.

[38] Tatano H, Tsuchiya S. A framework for economic loss estimation due to seismic transportation network disruption: A spatial computable general equilibrium approach. Natural Hazards, 2008, 44(2): 253-265

[39] Tatano H, Tsuchiya S. Tatano H, Kajitani Y. Economic impacts of the transportation network disruption: An extension of SCGE model. Methodologies for estimating the economic impacts of natural disasters, 2022, Singapore: Springer 85-95

[40] Tavasszy LA, Thissen M, Oosterhaven J. Challenges in the application of spatial computable general equilibrium models for transport appraisal. Research in Transportation Economics, 2011, 31(1): 12-18

[41] Tirasirichai, C. 2007. An indirect loss estimation methodology to account for regional earthquake damage to highway bridges. Ph.D. dissertation. University of Missouri-Rolla, Rolla, MO, USA.

[42] Tirasirichai C, Enke D. Case study: Applying a regional CGE model for estimation of indirect economic losses due to damaged highway bridges. The Engineering Economist, 2007, 52(4): 367-401

[43] Ueda, T., A. Koike, and K. Iwakami. 2001. Economic damage assessment of catastrophe in high speed rail network. In Proceedings of the 1st Workshop for Comparative Study on Urban Earthquake Disaster Management, 18–19 January 2001, Kobe, Japan, 13–19.

[44] Van Truong N, Shimizu T. The effect of transportation on tourism promotion: Literature review on application of the computable general equilibrium (CGE) model. Transportation Research Procedia, 2017, 25: 3096-3115

[45] Walmsley, T., A. Rose, R. John, D. Wei, J.P. Hlávka, J. Machado, and K. Byrd. 2022. Macroeconomic consequences of the COVID-19 pandemic. Economic Modelling 120: Article 106147.

[46] Wei, F., E. Koc, N. Li, L. Soibelman, and D. Wei. 2022. A data-driven framework to evaluate the indirect economic impacts of transportation infrastructure disruptions. International Journal of Disaster Risk Reduction 75: Article 102946.

[47] Wen Y, Zhang L, Huang Z, Jin M. Incorporating transportation network modeling tools within transportation economic impact studies of disasters. Journal of Traffic and Transportation Engineering (English Edition), 2014, 1(4): 247-260

[48] Wouter Botzen W, Deschenes O, Sanders M. The economic impacts of natural disasters: A review of models and empirical studies. Review of Environmental Economics and Policy, 2019, 13(2): 167-188

[49] Wu J, Li N, Hallegatte S, Shi P, Hu A, Liu X. Regional indirect economic impact evaluation of the 2008 Wenchuan earthquake. Environmental Earth Sciences, 2012, 65: 161-172

[50] Xia, Y., D. Guan, A.E. Steenge, E. Dietzenbacher, J. Meng, and D. Mendoza Tinoco. 2019. Assessing the economic impacts of IT service shutdown during the York flood of 2015 in the UK. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 475(2224): Article 20180871.

[51] Xie W, Rose A, Li S, He J, Li N, Ali T. Dynamic economic resilience and economic recovery from disasters: A quantitative assessment. Risk Analysis, 2018, 38(6): 1306-1318

[52] Yang L, Kajitani Y, Tatano H, Jiang X. A methodology for estimating business interruption loss caused by flood disasters: Insights from business surveys after Tokai heavy rain in Japan. Natural Hazards, 2016, 84(S1): 411-430

[53] Yang L, Chen Y, Jiang X, Tatano H. Multistate models for the recovery process in the Covid-19 context: An empirical study of Chinese enterprises. International Journal of Disaster Risk Science, 2022, 13(3): 401-414

[54] Yang L, Wang X, Jiang X. The impact of Hubei’s emergency response on regional economic system in the early stage of COVID-19 considering backward effect. Journal of Catastrophology, 2022, 37(198–204): 226 in Chinese

[55] Yu, K., R.R. Tan, and J.R. Santos. 2013. Impact estimation of flooding in Manila: An inoperability input-output approach. 2013 IEEE Systems and Information Engineering Design Symposium, 26 April 2013, Charlottesville, VA, USA, 47–51.

[56] Zhao Y, Kockelman KM. The random-utility-based multiregional input-output model: Solution existence and uniqueness. Transportation Research Part B: Methodological, 2004, 38(9): 789-807

[57] Zheng, H., Y. Bai, W. Wei, J. Meng, Z. Zhang, M. Song, and D. Guan. 2021. Chinese provincial multi-regional input-output database for 2012, 2015, and 2017. Scientific Data 8(1): Article 244.

[58] Zhou L, Chen Z. Are CGE models reliable for disaster impact analyses?. Economic Systems Research, 2021, 33(1): 20-46