Recently, the National Academy of Sciences issued a massive report analyzing how to cut U.S. gasoline use in half by 2030, and beyond that, how to reduce transportation emissions 80 percent by 2050.
If you don't want to read all 395 pages, Brad Plumer has an excellent write-up. The take-home message is that no one technology or policy -- efficiency, feebates, plug-in electrics, fuel cell vehicles, natural gas vehicles, what have you -- can get us to those goals. To make the numbers add up, you have to do them all together.
I don't have a bone to pick with any of the report's numbers, but I do want to use it to make a point.
In a previous post I made a distinction between widgets, which are the discrete elements of a system, and systems themselves. It is possible to model changes in widgets with some precision. You establish a baseline scenario ("business as usual") in which status quo policies, behaviors, and trends are locked into place and then model changes in a single variable, or small set of variables. This makes things manageable. If you set out to model changes in a large number of variables and how those changes interact with one another -- i.e., to model systemic changes -- things quickly become computationally intractable.
This is why modelers are always at pains to say that they are not making predictions, especially regarding scenarios stretching out to 2030 or 2050. Most such scenarios amount to, "what would happen if everything else stayed the same but this particular thing(s) changed?"
In the real world of human societies, of course, everything is changing at once. There are multiple overlapping complex systems, all of them displaying emergent properties and nonlinear dynamics. It makes predictions difficult -- especially, as they say, about the future.
You can't predict long-term changes in human cultures with models, though models can inform such predictions. Envisioning long-term change scenarios requires an understanding of history and political economy, serious engagement with culture and technology, a good grasp of systems dynamics, and a substantial dollop of intuition. Models can bring clarity to such analysis, but they cannot prove or disprove any scenario. Only time can do that.
Anyway, why am I blathering on about this? Because I fear that people take reports like the NAS one the wrong way, as a kind of delineation of what's possible. It is not that. It merely demonstrates the limits of widget-based analysis.
Plumer notes that the report passes almost entirely over changes in land use and public transit, but I think that's just one aspect of a larger phenomenon, which is that the report confines itself entirely to changes in vehicles. (To be fair, that's what lots of people seem to do when imagining changes in transportation.)