The long-term aim of the present project is to construct a large simulation model that can be used to predict how poor countries, and regions within poor countries, are affected by global warming. One feature of the model is that it predicts economic outcomes and climate outcomes jointly: it is an “integrated” assessment model. The main novelty, compared to existing models of a similar kind, is the high degree of resolution: predictions for climate change and economic outcomes will thus be made on a very fine global grid consisting of thousands of “regions”. This level of geographic detail enables us not only to examine differential impacts of climate change across different regions, but also to evaluate different adaptation mechanisms (such as trade, insurance and migration) that involve explicit interaction between regions. In addition, this level of detail allows us to provide quantitative evaluations of climate-related policies that involve worldwide interactions of different regions, such as international trading of carbon permits and differences in carbon taxes across regions. The ultimate aim is to use 1-by-1 (longitude-by-latitude) degree cells. The high degree of resolution can be obtained by taking advantage of recent advances in computing technology, by using state-of-the-art numerical tools for simulating the behaviour of macroeconomic models with a large number of actors, and by collaborating, on the climate side, with natural scientists who are experts on climate modelling.
The construction of the model is under way. The project so far has involved successful model-solving and simulation. It was confirmed that it is indeed feasible to operate at this high level of resolution. The next stage, which will be begun shortly, is to solve a quantitatively restricted version of the model. The quantitative restrictions will be informed by the study of past weather and economic data across the thousands of regions around the world, as well as climate projections for the future available from high-resolution global-circulation meteorological models.
To assess how weather and climate variations differ across the regions, annual data from the IPCC was investigated. Thus, with temperature observations on 5-by-5 degree cells, relationships between local and global temperatures were estimated. It turns out that different regions have very different responses, or covariation, with the global temperature. When the global temperature rises, some regions respond much more than one-for-one: temperatures there go up by much more than the global temperature increase. By the same token, other regions are less sensitive to global temperature changes. Weather variations on a regional level, moreover, are large, long-lasting, and correlated across space.
Using economic data for 1-by-1 degree cells from the G-Econ database, it was also possible to link economic outcomes (GDP) to weather outcomes. This research thus cannot explain through what channels production is influenced by weather, but it can say something about the extent to which it is. A central finding is that there is a significant link between temperature and output in the cross-section of regions. Controlling for the country in which a region resides, a statistically and quantitatively significant effect of decadal average temperature on GDP/capita was established: a 1-degree regional temperature increase decreases regional GDP/capita by about 0.9%.
Finally, panel data was used to relate average five-year growth rates of regional GDP/capita (aggregated to 5-by-5 degree cells) to average temperatures during the previous five years. There are statistically significant but quantitatively relatively small effects of temperature shocks on either levels or growth rates of GDP/capita in the panel. These relationships, moreover, are not discernibly different across rich and poor regions.