In this paper, we take a global view at air pollution looking at cities and countries worldwide. We pay special attention at the spatial distribution of population and its relationship with the evolution of emissions. To do so, we build i) a unique and large dataset for more than 1200 (big) cities around the world, combining data on emissions of CO2 and PM2.5 with satellite data on built-up areas, population and light intensity at night at the grid-cell level for the last two decades, and ii) a large dataset for more than 190 countries with data from 1960 to 2010. At the city level, we find that denser cities show lower emissions per capita. We also find evidence for the importance of the spatial structure of the city, with polycentricity being associated with lower emissions in the largest urban areas, while monocentricity being more beneficial for smaller cities. In sum, our results suggest that the size and structure of urban areas matters when studying the density-emissions relationship. This is reinforced by results using our country-level data where we find that higher density in urban areas is associated with lower emissions per capita. All our main findings are robust to several controls and different specifications and estimation techniques, as well as different identification strategies.

在本文中，我们以全球视角看待空气污染，着眼于世界各地的城市和国家。我们特别关注人口的空间分布及其与排放演变的关系。为此，我们为全球 1200 多个（大城市）构建了一个独特的大型数据集，将 CO2 和 PM2.5 排放数据与建成区、人口和夜间光照强度的卫星数据相结合在过去二十年的网格单元级别，以及 ii) 包含 1960 至 2010 年数据的 190 多个国家的大型数据集。在城市级别，我们发现人口稠密的城市人均排放量较低。我们还发现了城市空间结构重要性的证据，多中心性与最大城市地区的低排放相关，而单中心对小城市更有利。总之，我们的结果表明，在研究密度-排放关系时，城市地区的规模和结构很重要。使用我们国家级数据的结果加强了这一点，我们发现城市地区的较高密度与较低的人均排放量有关。我们所有的主要发现对于几种控制和不同的规范和估计技术以及不同的识别策略都是稳健的。