The A.I. disruption and the need to compete on innovation are impacting
cities that have an increasing necessity to become innovation hotspots.
However, without proven solutions, experimentation, often unsuccessful, is
needed. But experimentation in cities has many undesirable effects not only for
its citizens but also reputational if unsuccessful. Digital Twins, so popular
in other areas, seem like a promising way to expand experimentation proposals
but in simulated environments, translating only the half-baked ones, the ones
with higher probability of success, to real environments and therefore
minimizing risks. However, Digital Twins are data intensive and need highly
localized data, making them difficult to scale, particularly to small cities,
and with the high cost associated to data collection. We present an alternative
based on synthetic data that given some conditions, quite common in Smart
Cities, can solve these two problems together with a proof-of-concept based on
NO2 pollution.

研究新的解决方案，以在智慧城市中实现试验和创新，使用数字孪生技术和合成数据，以最小化风险和成本，并以 NO2 污染为例进行了论证。