Este modelo proporciona una estimación global consistente, resuelta espacialmente (250m), de las huellas de carbono (también denominadas emisiones Scope 3) en términos per cápita y absolutos en 189 países. Incorpora modelos subnacionales existentes para Estados Unidos, China, Japón, UE y Reino Unido.
This model provides a globally consistent, spatially resolved (250m), estimate of carbon footprints (also called Scope 3 emissions) in per capita and absolute terms across 189 countries. It incorporates existing subnational models for the US, China, Japan, EU, and UK. The model is described in the open-access publication Carbon footprints of 13,000 cities.
This article from Scientific American also gives a nice overview of the study. The results have also been covered by World Economic Forum, Scientific American, NASA’s Earth Observatory blog, US News & World Report, National Geographic Russia, among others.
Key Findings
One observation that can be made based on this model is in many countries, a small number of large and or affluent cities drive a significant share of national total emissions. This means concerted action by a small number of local mayors and governments has the potential to significantly reduce national total carbon footprints. Some other observations based on the results include:
- Globally, carbon footprints are highly concentrated into a small number of dense, high-income cities and affluent suburbs
- 100 cities drive 18% of global emissions
- In most countries (98 of 187 assessed), the top three urban areas drive more than one-quarter of national emissions
- We define cities as population clusters, but in practice mapping footprints to local jursidictional bounds is complex
- 41 of the top 200 cities are in countries where total and per capita emissions are low e.g. Dhaka, Cairo, Lima). In these cities population and affluence combine to drive footprints at a similar scale as the highest income cities
- For large and high-income cities, their total Scope 3 footprint is much larger than the city’s direct emisisons
- Radical decarbonization measures (limiting nonelectric vehicles; requiring 100% renewable electricity) can induce substnatial emissions reductions beyond city boundaries. In wealthy, high-consumption, high-footprint localities such measures may require only a small investment relative to median income, yet accomplish large reductions in total footprint emissions
- Local action at the city and state level can meaningfully affect national and global emissions
Carbon Footprints of World Cities
The model estimates the carbon footprint (CF) of individual cities. When interpreting the results please keep in mind that the results from a global top-down model will never be as precise as more detailed local or bottom-up assessments. Additionally, defining the city population and bounds is hard. We use the GHS-SMOD definition of “cities” as contiguous population clusters. This does not correspond directly to the precise legal jurisdiction for many cities. Additionally, the population within these clusters may include exurbs and other areas and may not correspond to the city’s official population. Taking the city’s estimated population as given, model provides results for each city with an associated uncertainty range. The 1 standard deviation value is shown. The mean per-capita CFs are also shown as a median estimate with an associated uncertainty range.
The CF of each cluster is reported as a mean estimate with a standard deviation. These can be interpreted in the normal way: e.g. if the Footprint of a city is reported as 20 ±5, it means there is a 67% chance the city’s CF is between 15-25, and a 95% chance the CF is between 10 and 20. Details about how these are calculated are provided in the paper and the SI.
Important note about the city names: The GGMCF is fundamentally based on the EU’s GHS-SMOD gridded population model. The urban clusters are identified on top of that gridded population model, and then we attempted to label the cities corresponding to each cluster. To get the city names we use the NORPIL list of cities and ESRI’s World Cities database. This matching is not perfectly accurate. In some cases the name is picked up from a closely adjacent city, or from an annex suburb that is contiguous with a larger city. Many clusters we have not yet been able to name; these appear as simply “Unnamed city” in the list.
Below are tables with the top 500 cities, by absolute CF and by CF per capita. See below for download links.
Fuente: http://citycarbonfootprints.info
