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Tropical cyclones (TCs) can adversely affect economic development for more than a decade. Yet, these long-term effects are not accounted for in current estimates of the social cost of carbon (SCC), a key metric informing climate policy on the societal costs of greenhouse gas emissions. We here derive temperature-dependent damage functions for 41 TC-affected countries to quantify the country-level SCC induced by the persistent growth effects of damaging TCs. We find that accounting for TC impacts substantially increases the global SCC by more than 20%; median global SCC increases from US$ 173 to US$ 212 per tonne of CO 2 under a middle-of-the-road future emission and socioeconomic development scenario. This increase is mainly driven by the strongly TC-affected major greenhouse gas emitting countries India, USA, China, Taiwan, and Japan. This suggests that the benefits of climate policies could currently be substantially underestimated. Adequately accounting for the damages of extreme weather events in policy evaluation may therefore help to prevent a critical lack of climate action. The estimates of the societal costs of carbon currently used for policy evaluations may be too low due to an insufficient representation of tropical cyclone damage. Accounting for them substantially increases the estimated benefits of climate change mitigation measures.

Economies are frequently affected by natural disasters and both domestic and overseas financial crises. These events disrupt production and cause multiple other types of economic losses, including negative impacts on the banking system. Understanding the transmission mechanism that causes various negative second-order post-catastrophe effects is crucial if policymakers are to develop more efficient recovery strategies. In this work, we introduce a credit-based adaptive regional input-output (ARIO) model to analyse the effects of disasters and crises on the supply chain and bank-firm credit networks. Using real Japanese networks and the exogenous shocks of the 2008 Lehman Brothers bankruptcy and the Great East Japan Earthquake (March 11, 2011), this paper aims to depict how these negative shocks propagate through the supply chain and affect the banking system. The credit-based ARIO model is calibrated using Latin hypercube sampling and the design of experiments procedure to reproduce the short-term (one-year) dynamics of the Japanese industrial production index after the 2008 Lehman Brothers bankruptcy and the 2011 Great East Japan earthquake. Then, through simulation experiments, we identify the chemical and petroleum manufacturing and transport sectors as the most vulnerable Japanese industrial sectors. Finally, the case of the 2011 Great East Japan Earthquake is simulated for Japanese prefectures to understand differences among regions in terms of globally engendered indirect economic losses. Tokyo and Osaka prefectures are the most vulnerable locations because they hold greater concentrations of the above-mentioned vulnerable industrial sectors.

This work aims to study and explain the business cycle correlations of the Japanese production network. We consider the supplier-customer network, which is a directed network representing the trading links between Japanese firms (links from suppliers to customers). The community structure of this network is determined by applying the Infomap algorithm. Each community is defined by its GDP and its associated business cycle. Business cycle correlations between communities are estimated based on copula theory. Then, based on firms' attributes and network topology, these correlations are explained through linear econometric models. The results show strong evidence of business cycle correlations in the Japanese production network. A significant systemic risk is found for high negative or positive shocks. These correlations are explained mainly by the sector and by geographic similarities. Moreover, our results highlight the higher vulnerability of small communities and small firms, which is explained by the disassortative mixing of the production network.

This paper studies the driving forces behind the formation of ties within the major communities in the Japanese nationwide network of production, which contains one million firms and five million links between suppliers (“ upstream \" firms) and customers (“ downstream \" firms). We apply the Infomap algorithm to reveal the hierarchical structure of the production network. At the second level of the hierarchy, we find a reasonable community resolution, where the community size distribution follows a power law decay. Then, we estimate the tie formation within 100 communities of different sizes. The studied model considers a large set of attributes, including both endogenous attributes (network motifs, e.g., stars and triangles) and exogenous attributes (economic variables, e.g., net sales and firm size). The estimation results show that the considered model converges and presents a high goodness of fit (GoF) for all communities. Moreover, it is found that the forces explaining the formation of links between suppliers and customers differ among communities. Some attributes, such as reciprocity, popularity, activity, location homophily, bank homophily and sales statistics, are common drivers of internal link formation for most of the studied communities. However, transitivity is rejected as a significant influencing factor for most communities, reflecting an absence of a sense of trust and reliability between firms with a common partner. Finally, we show that sector homophily does not serve as an obvious mechanism of partnership at the community level in the production network.

This work presents an artificial order-driven market able to reproduce mature and immature stock markets properties in the case of a single traded asset. This agent-based artificial market is designed to simulate characteristics of immature stock markets (high risk and low efficiency) by reproducing their stylized facts related mainly to information asymmetry and herd behavior. These two properties are modeled by combining social network and multi-agent simulations. The constructed scale-free social network, linking the modeled investors, gives rise to both informed and uninformed agents communities. Different assortative topologies are proposed and linked to different degrees of information asymmetry and market maturities. Several simulation experiments show that the modeled information asymmetry and herd behavior succeed in reproducing artificially some important stylized facts characterizing differences between immature and mature stock markets.

This paper aims mainly at building artificial stock markets with different maturity levels by modeling information asymmetry and herd behavior. The developed artificial markets are multi-assets, order-driven and populated by agents having heterogeneous behaviors and information. Agents are defined by their information and their herd behavior levels. Agents trade multiple risky assets based on their wealth, their behaviors and their available information which spread among multiple behavioral networks. In a novel contribution to artificial stock markets literature, agents’ behaviors modeling is mixed with social network simulation to reproduce different degrees of information asymmetry and herd behavior based on several assortative topologies. Several simulations validated the proposed model since univariate and multivariate stylized facts were reproduced both for mature and immature stock markets. The proposed artificial stock market can be considered as a first step toward decision and simulation tools for optimal management, strategy analysis and predictions evolution of immature stock markets.

We use the exponential random graph models to understand the network structure and its generative process for the Japanese bipartite network of banks and firms. One of the well known and simple model of exponential random graph is the Bernoulli model which shows the links in the bank-firm network are not independent from each other. Another popular exponential random graph model, the two star model, indicates that the bank-firms are in a state where macroscopic variables of the system can show large fluctuations. Moreover, the presence of high fluctuations reflect a fragile nature of the bank-firm network.

Inter-firm organizations, which play a driving role in the economy of a country, can be represented in the form of a customer-supplier network. Such a network exhibits a heavy-tailed degree distribution, disassortative mixing and a prominent community structure. We analyze a large-scale data set of customer-supplier relationships containing data from one million Japanese firms. Using a directed network framework, we show that the production network exhibits the characteristics listed above. We conduct detailed investigations to characterize the communities in the network. The topology within smaller communities is found to be very close to a tree-like structure but becomes denser as the community size increases. A large fraction (~40%) of firms with relatively small in- or out-degrees have customers or suppliers solely from within their own communities, indicating interactions of a highly local nature. The interaction strengths between communities as measured by the inter-community link weights follow a highly heterogeneous distribution. We further present the statistically significant over-expressions of different prefectures and sectors within different communities.

This work aims to explain how firms behave and select their suppliers and customers in the Japanese production network. We study a supplier-customer network of listed firms in Japan (3,198 firms with 20,417 links). In order to specify how firms choose their partners, the so-called exponential random graph model is applied to estimate the ties formation process. For the estimation of such a large-scale network, we employ a recent technique of sampling called the improved fixed density Markov Chain Monte Carlo (MCMC). Our main result shows that all of the effects (social and economic effects) are statistically significant in explaining the ties formation between firms. Social effects such as mutuality and transitivity with common partners in different directional links between suppliers and customers are shown. Moreover, homophily with the same industrial sectors and geographical locations, and disassortative mixing between low-profit firms and high-profit ones are also found. We argue that our method is extended to the spatially heterogeneous structure of communities reflecting industrial sectors and geographical locations and temporal changes of supplier-customer relationships in such a framework of the stochastic actor-oriented model.