06 March 2017
Geo-Simulations as a Policy Toolkit for Natural Disasters
Adverse post-natural disaster outcomes in low-income regions, like elevated internal migration levels, and low consumption levels are the result of market failures, poor mechanisms for stabilizing income, and missing insurance markets, which force the affected population to respond, and adapt to the shock they face. In a spatial environment, with multiple locations with independent but inter-connected markets, these transitions quickly become complex and highly non-linear due to the feedback loops between the micro individual-level decisions and the meso location-wise market decisions. To capture these continuously evolving micro-meso interactions, a spatially-explicit bottom-up agent-based model to analyze natural disaster-like shocks to low-income regions is discussed here. The aim of the model is to temporally and spatially track how population distributions, income, and consumption levels evolve, in order to identify low-income workers that are "food insecure''. Model results show, how various factors like existing income and saving levels, distance from the fault line, and proximity to other locations, can give insights on the spatial and temporal emergence of vulnerabilities. The simulation framework presented here, leaps beyond existing modeling efforts, which usually deals with macro long-term loss estimates, and allows policy makers to come up with informed short-term policies in an environment where data is non-existent, policy response is time dependent, and resources are limited.
An Integrated Approach to Modeling Growth, Finance, and the Environment
A key challenge in high income countries is how to achieve growth without increasing the climate impact. While several solutions have been proposed in literature from market-based carbon pricing mechanisms to more centralized “command-and-control” government regulations, higher investment in green technologies to innovation policies, several questions still remain unanswered: what drives innovation when resources are limited, who finances it, and which climate policies work under such scenarios? To address these concerns, a new approach is used: stock-flow consistent (SFC) macro modeling which allows for fully tracking financial flows within an economic system across multiple sectors – firms, households, government, and commercial banks. The framework incorporates behavioral rules with cross-sector linkages that allows for tracking the feedback of one sector on other sectors. This framework can be easily integrated with the national system of accounts (NSA) for policy analysis. Two models are presented here. The first model discusses induced technological change across multiple inputs with budget constraints and tests for different types of climate policies including higher environmental tax, and higher R&D investment. The second model discusses North-South linkages with focus on the impact of innovation and climate policies in the global north on countries in the global south.
Last edited: 08 March 2017
Postdoctoral research at IIASA
International Institute for Applied Systems Analysis (IIASA)
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