Urban areas are responsible for over 70% of the world's carbon emissions from energy use. The largest contributors to these emissions are transport and buildings. Inversing the spatial distribution of carbon emissions at microscopic scales such as communities or plots can capture the critical emission areas, thus providing scientific guidance for intracity low-carbon development planning.
However, previous studies mostly focus on global, national, or provincial scales, lacking uniform accounting data and methods to downscale the carbon emissions within cities.
Now, researchers from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences (CAS), together with collaborators from the China University of Mining and Technology-Beijing, constructed a valid approach to detect spatial distribution of urban carbon emissions at micro level.
It uses the Sino-Singapore Tianjin Eco-city as an example to measure and analyze the spatial carbon emission efficiency. The Tianjin eco-city is a flagship project launched by Singapore and China with the goal to build a model city for sustainable development.
The researchers used night-light images and statistical yearbooks to perform linear fitting within the Beijing-Tianjin-Hebei city-county region, and then uses a fine-scale data such as points of interest, road networks and mobile signaling data to downscale the carbon emissions to the plots — the basic administrative units of the city.
Results showed that among the selected indicators, the population distribution significantly influenced carbon emissions, and the spatial distribution of carbon emissions was relatively distinctive.
The study realizes fine-scale comparison of carbon emissions spatially, and highlights that population distribution is the key indicator that influences carbon emissions in a city.
The methodology in this research is general and applicable to the study of the spatial distribution of carbon emissions in other cities. It provides a new thought to assign carbon emissions spatially at a finer scale.