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Event-based modeling of economic impacts of climate change

The economic impacts of climate change are expected to depend on the spatial and temporal patterns of weather fluctuations (extreme events or long-term trends) in a potentially complex way. On the one hand, in disaster-prone regions the economy may not be able to recover in between recurring climate extremes or reconstruction efforts may exceed national coping capacities raising debt sustainability concerns and potentially leading to poverty traps, especially in developing and emerging countries. On the other hand, since regional economies are embedded in a complex network of trade relations, local disasters can have global repercussion. In particular, the interaction of cascading supply failures induced by coinciding climate extremes may enhance overall losses, even if these events occur in different parts of the world.

With the availability of cross-sectoral consistent and temporally and spatially explicit bio-physical impact projections, we are now in the position to address these climate risks in a RCP-SSP scenario framework. Thereby, we focus on different types of risks, ranging from short-term to long-term socio-economic impacts:

  1. sector- and event-specific direct asset losses.
  2. price spikes and supply-failures on global agricultural world markets causing regional food-insecurities.
  3. the spreading of disaster induced losses in the global economic network comprising direct as well as indirect losses.
  4. transformational risks of climate policy instruments such as adverse distributional impacts or shifts in competitiveness and trade relations.
  5. long-term impacts on the economic development trajectories of countries including possible debt crises.


This working group aims to gain a deep process understanding of the main impact channels of climate extremes on the socio-economic system by combining empirical methods with dynamic macroeconomic modeling. We develop empirical methods to quantify climate change impacts on the socio-economic development pathways of countries, ranging from short-term impacts such as asset losses to long-term impacts on economic growth. Driven by these empirical insights, we develop macroeconomic models that particularly accounting for potential non-linearities and long-term effects induced by incomplete recovery in-between events. Filling the gap between linear empirical models and computationally expensive modeling approaches, we focus on a simplified representation of the underlying dynamics that - by calibrating to observations - nevertheless allows for quantitative assessments. With these modeling approaches, we aim to i) enhance our process-understanding, ii) capture non-linearities in future projections, iii)  assess viable coping strategies, and iv) improve the representation of these extremes in large-scale integrated assessment models such as REMIND.


Christian Otto (leader)

Tobias Geiger

Annika Günther

Johannes Gütschow

Louise Jeffery

Inga Sauer

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