Ever wonder just how much carbon you dump into the atmosphere? Would it be more fun to know the answer if you were also given the tools to reduce it? The Coolclimate Network, a division of UC Berkeley's Renewable and Appropriate Energy Laboratory (RAEL), offers one form that takes just minutes to fill out, and comes with a toolbox full of ideas for reducing those emissions. The API integrates the survey into third party apps. The Coolclimate Calculator joins 70 other APIs in our directory focused in the environment.
From the graphic above, you can get the gist--selecting different cities, different numbers of family members, different incomes all modifies the total tonnage figure given at the lower right. (The more you earn, the more you burn, at least in terms of greenhouse gas, according to the survey.)
The graphic above is just the introductory screen; the list across the top, Intro, travel, housing, etc., are tabs to look at the carbon emissions created by those activities. As you fill out the survey, you are measured against the averages in the city you have selected. (Not all cities are represented; being from a rural area, I picked Cambridge, MA, in order to see how it worked.)
In the graphic below, from the last tab, the Take Action tab on the upper right, there is acute interactive feature: as you pledge to do various things (the first one being "Buy a More Efficient Vehicle") the unhappy smiley character on the right turns happy.
Selecting all pledges eliminates the carbon footprint entirely. This seems... unlikely. After all, both an efficient vehicle and public transportation still add emissions.
And yet, it might be the right track; the start of a great deal of change is measurement, or at least observation. No great injustice has ever been eliminated without first observing it, for example. Even if it isn't perfect, Coolclimate gives us a yardstick. We can imagine the day when the data that developers and users can play with has a great deal more precision. And we can also see that manipulating the data set might be done in substantially the same way as it is here, given that Coolclimate has presented it so clearly.