Ev o lvi n g Co m par at ive Advantages and the Impac ts of E xtr em e Weat he r Eve nt s
BOX 3.1 Numerical Model to Explore the Economic Impacts of Compound Hazards and Trade Restrictions The impact model stems from the adaptive regional input-output (ARIO) model, which is widely used in the realm of single-hazard analysis to simulate the propagation of negative shocks throughout the economy (Hallegatte 2014; Hallegatte and Ghil 2008). Compared with traditional input-output (IO) and computable general equilibrium (CGE) models, the ARIO model is an agent-based model that offers the simplicity of IO modeling and has the flexibility of CGE modeling. This model is extended to allow for cross-regional substitutability of suppliers, to be able to assess the global supply-chain effects of COVID-19 control measures and flood responses. The model analyzes the interaction between climate and pandemic responses—that is, the negative externality of pandemic control for the recovery of capital destroyed by natural disasters and the stimulus effects of capital reconstruction to offset the negative impacts of pandemic control. Second, it considers the role of export restrictions and production specialization in exacerbating the economic consequences of the compound events. It does so by varying the substitutability of regional products to investigate the effect of production specialization in different sectors. This compound-hazard impact model is applied to a hypothetical global economy that consists of four regions and five sectors. The four regions—regions A, B, C, and D—account for 21 percent, 39 percent, 28 percent, and 12 percent of the global economy, respectively. Region C is the only region that is hit by flooding amid a global pandemic. Region B is the largest trading partner of region C. More than half (52 percent) of C’s total trading volume—equivalent to 11 percent of C’s output—comes from region B. Next in line are regions A and D, accounting for 31 percent and 17 percent of C’s total trading volume, respectively. The five sectors are agriculture, general manufacturing, capital manufacturing, construction, and other services. Capital manufacturing and construction are the two sectors involved in reconstructing capital damaged by flooding. It is assumed that capital reconstruction relies largely on local inputs of capital goods and construction services. For example, the construction and capital manufacturing sectors of region C account for 68 percent and 20 percent of the reconstruction in region C, respectively, while the capital manufacturing and construction sectors of region B and the capital manufacturing sector of region A account for the remaining 12 percent of construction. The model is run on a weekly basis in this study. Three scenarios of flooding are explored: a small flood, affecting 20 percent of the population in region C; a medium flood, affecting 40 percent of its population; and a large flood, affecting 60 percent. At the same time as the flooding occurs, regions affected by the pandemic take measures to bring its spread under control. The strictness of the control policy, which is measured by the percentage reduction in transportation capacity due to lockdown measures relative to the predisaster level, is benchmarked at 30 percent for 24 weeks. The direct economic impacts of the collision of these disasters arise from (1) the shortage or malfunction of production factors (productive capital and labor), reducing firms’ production capacity; (2) the impact on infrastructure, specifically on transportation critical to linking the supplies and demands of different agents in the economic networks, because transportation failures increase the inaccessibility to production materials and interrupt production activities; and (3) the impact on final demands, leading to structural, short-term changes in the overall mix during or after the event. Export restrictions are included progressively in the form of a 25 percent, 50 percent, and 75 percent reduction in export volume. For more details on the model and the scenarios, see and AghaKouchak et al. (2020) and Hu et al. (2021).
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