An ambitious undertaking to say the least, but they point out a few interesting findings as evidence that such an effort might be possible. To wit:
Three main characteristics vary systematically with population. One, the space required per capita shrinks, thanks to denser settlement and a more intense use of infrastructure. Two, the pace of all socioeconomic activity accelerates, leading to higher productivity. And three, economic and social activities diversify and become more interdependent, resulting in new forms of economic specialization and cultural expression.
We have recently shown that these general trends can be expressed as simple mathematical ‘laws’. For example, doubling the population of any city requires only about an 85% increase in infrastructure, whether that be total road surface, length of electrical cables, water pipes or number of petrol stations. This systematic 15% savings happens because, in general, creating and operating the same infrastructure at higher densities is more efficient, more economically viable, and often leads to higher-quality services and solutions that are impossible in smaller places.
So far, pretty common sense. A friend once remarked to me that New York's density subsidizes US Postal Service deliveries to rural towns with populations in the 100s--dropping mail to addresses with dozens of residents is certainly more cost efficient than dropping mail to single resident addresses several miles apart.
What gave me pause was the mathematical regularity the researchers found:
The bigger the city, the more the average citizen owns, produces and consumes, whether goods, resources or ideas. On average, as city size increases, per capita socioeconomic quantities such as wages, GDP, number of patents produced and number of educational and research institutions all increase by approximately 15% more than the expected linear growth. There is, however, a dark side: negative metrics including crime, traffic conges- tion and incidence of certain diseases all increase following the same 15% rule. The good, the bad and the ugly come as an integrated, predictable, package.
The article eventually reasons that the most useful application of this data will be to evaluate policy successes and failures--slightly more modest than a "Unified Theory", but a chance, nonetheless, at a concrete metric in a field where data can be diffuse, difficult to capture, or subject to all sorts of faulty assumptions (see the discussion about the problems with density here).
So ideally, a city manager in x city would be able to look at a model predicting incidences of crime, patents, or Vehicle Miles Traveled, or whatever, and be able to see if his/her city is under or overperforming its expected tally. And then use this as evidence to help guide new policy initiatives.