Global warming is going to increase the costs of maintaining Alaska's public infrastucture (roads, bridges, sewage systems, buildings, airports).
Researchers from the University of Alaska Anchorage's Institute for Social and Economic Research look into it: Estimating Future Costs for Alaska Public Infrastructure At Risk From Climate Change:
A warming climate will damage Alaska's infrastucture because it was designed for a cold climate. The damage will be concentrated in places where permafrost thaws, flooding increases, and coastal erosion gets worse. But the extra costs will likely diminish over time, as government agencies increasingly adapt infrastructure to changing conditions.
They estimate that the present value of costs through 2030 may rise by 10% to 20%. Thirty percent of the extra costs would come from repair of water and sewage systems, and about 25% each for roads and airport runways. Harbors account for about 8%.
The authors created a data set of 15,665 elements of Federal, State, and local public infrastructure in Alaska - buildings, roads, power lines, airports, harbors, and so on. The location of each item, its expected lifetime under current conditions, and the cost of replacement, were noted.
The model works by assuming that, as temperatures rise, the expected lifetime of any given piece of infrastructure is reduced. Costs increase because, within any given time period, the infrastructure will have to be replaced more often.
The analysis takes account of the fact that we are going to learn about the actual form that global warming will take in Alaska, and about how to build under the new conditions. So the proportional impact over the long term (out to 2080) is likley to be less than in the medium term (out to 2030):
Damage from climate change could add $3.6 to $6.1 billion (10% to 20%) to future costs for public infrastructure from now to 2030 and $5.6 to $7.6 billion (10% to 12%) from now to 2080. These estimates take into account different possible levels of climate change and assume agencies adapt infrastrucure to changing conditions.
The incorporation of learning is crude, but still kind of neat. The authors adopt an approximation to "event driven" adaptation to floods they observe in the Midwest:
...flood response is primarily an event-driven model. In this form of model, adaptive responses are not put into place until there is a flood that significantly reduces the useful life of infrastructure. Local authorities determine when that threshold has been crossed. If they decide a specific flood didn’t reach that threshold, then they don’t take any action, with the understanding that this event is part of the natural cycle. New structures are constructed with the same specifications as existing ones, since they are still considered the standard. By contrast, if there is a flood that exceeds what is considered part of the natural cycle, then a rapid adaptation response is put into place, resulting in a change of building codes that requires a stair-step increase in building costs. This response is seen as indirectly based on the rate at which states report and classify floods and the resulting responses to those reports (Pielke, Downton, and Miller 2002)
Similar to the response to earthquakes, the response model for floods is code-based. Rather than the private market driving structural changes, flood response results in regulatory changes. The regulatory changes are codified through building code updates. However, since the code is only updated after a significant event, the regularity of the updates does not follow the pattern seen with earthquakes. The result is an expense that occurs less frequently but tends to have a greater effect when it does occur.
Here's how it's implemented:
The concept of the event-driven model is that adaptation research is being conducted, but no action is taken for a particular structure until damage reduces the life span enough to reach some critical threshold. Until that point, it is assumed additional repair money could maintain a reasonable useful life span. The threshold used in this model is 20%. We adopted that percentage based on a rule of thumb in planning—that once a building loses 20% of its useful life, economically it becomes more feasible to rebuild than repair. However, this number should be considered an estimated value for this model and not an absolute for every scenario.
In this model, the life span of the structure is affected by the impact template, based on precipitation and temperature increases. For example, a hospital with a 40-year life span that is affected by both temperature and precipitation increases will have its life span shortened, based on the appropriate database entries. In the current database scenario, the worst case is a hospital losing about 5% of its life span for each 1 degree of temperature increase and each 1-inch increase in precipitation. Given that the average model shows a temperature increase of 2.1 degrees by 2030 and a 4.3% increase in precipitation (.688 inches in Anchorage), that results in a total life span reduction of 15%, with 11.5% for temperature and 3.5% for precipitation.
In the event-driven model, the first generation of the 40-year hospital is reduced by 15%, or six years, resulting in a useful life of 34 years. At this point, the event-driven model determines if the useful life reduction threshold has been met. If the threshold has not been reached, then a new structure is built with a reduced life span expectancy. In this case it would be 34 years to start with, rather than the original 40 years. If the threshold has been reached, then a cost increase of 5% is absorbed and the structure is built with the original life span.
The effect of the event-driven model becomes increasingly apparent as the potential useful life of the structure becomes shorter. For example, in the case of a water treatment facility with a 20-year life span, the difference becomes apparent in only three generations. In this example, the first generation is affected by climate change that reduces the facility life span by 14.3%, or 3.5 years. Since this loss is below the 20% threshold, the same structure is built with no climate-change-adjusted costs, but with an expected useful life of only 16.5 years. This results in the second generation structure lasting only 14.3 years, since it started with an anticipated useful life of only 16.5 years. However, at this point the useful life reduction now exceeds the 20% reduction threshold. Thus, the third generation structure incorporates a 5% cost increase to return to the original 20-year life expectancy.