CityReads│Mapping the Carbon Footprints of 13,000 Cities

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Mapping the carbon footprints of 13,000 cities

New study estimates the carbon footprints of 13,000 cities.

Moran, D., Kanemoto K; Jiborn, M., Wood, R., Többen, J., and Seto, K.C. (2018) Carbon footprints of 13,000 cities. Environmental Research Letters DOI: 10.1088/1748-9326/aac72a.

Source: http://iopscience.iop.org/article/10.1088/1748-9326/aac72a/pdf

Cities are home to the majority of the current and predicted global population, economic activity, and energy use. Urban areas are home to about 54% of total global population and account for more than 70% of global energy use. 600 urban centers generate about 60% of global GDP.

But we don't know how this power is distributed between cities, or between cities and suburbs Some cities have footprint accounts, but these use quite different methods and are not consistent. For a few countries (USA, Australia, Norway UK) consistent urban and suburban footprints have been calculated. But for most cities and countries in the world, this has not been done.

What is the role of poor, populous megacities?

What is the role of small, rich cities?

To answer these questions, Researchers from the Norwegian University of Science and Technology (NTNU) have surveyed the carbon footprints of 13,000 cities worldwide. They built a model of how carbon footprints are distributed in the world. This is the first time anyone has drawn a complete map of potential carbon footprint reductions from cities.

This model provides a globally consistent, spatially resolved (250m), estimate of carbon footprints (CF) in per capita and absolute terms across 189 countries. It incorporates existing subnational models for the US, China, Japan, EU, and UK. In order to examine the spatial distribution of carbon footprints at the household level, They developed a top-down, globally consistent gridded model. The model uses gridded population and income data to disaggregate existing subnational carbon footprint models. The model uses urban vs. rural consumption patterns and purchasing power as the main predictors of per capita footprint.

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.

Key findings:

Globally, carbon footprints are highly concentrated into a small number of dense, high-income cities and affluent suburbs

The top 100 urban areas by carbon footprint contain 11% of the world’s population but drive 18% of the global CF.

In most countries a few urban areas account for a disproportionate share of the total footprint. In 98 of the 187 countries assessed, the top three urban areas drive more than one-quarter of the national CF. In the US and China, ten cities drive approximately one-third of the national carbon footprint

While many of the urban areas with the highest CF are in countries with high carbon footprints, 41 of the top 200 (e.g. Dhaka, Cairo, Lima) are in countries where total and per capita emissions are low (e.g. Senegal, Egypt, Peru). In these urban areas, population and affluence combine to drive footprints at a similar scale as counterparts in the highest income countries.

Cologne, Manchester, and Montreal rarely make the list of the world’s megacities. Yet they are all in the top 100 worldwide in terms of their carbon footprint. 

For large and high-income cities, their total Scope 3 footprint is much larger than the city's direct emissions. Radical decarbonization measures (limiting nonelectric vehicles; requiring 100% renewable electricity) can induce substantial 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

In addition to the role of key large and/or affluence cities in driving the global CF, the contribution of affluent, low-density areas is also clear. The top 5% of non-urban residents globally (by CF per capita) generate 32% of the entire national footprint in the US, and a similar share (21%) in China. In those two countries, the top ten urban plus top 5% of suburban residents drive more than half of the national carbon footprint. In most countries, however, even the most footprint-intensive suburbs are outshone by the scale of consumption in urban centers

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. Many of the cities with the highest footprints are also the richest, and thus have plenty of power to do something about it.

Cities with the biggest carbon footprints also have the most power to make big changes.

The fast-growing cities today contribute a minority share to the global footprint, but this can be expected to change as those cities grow in terms of infrastructure, population, and affluence.

Conclusion

A small number of cities can have big impacts on national emissions. The confluence of high concentration of global GDP and global CFs augurs well for future development of innovative strategies to reduce footprints. The fact that CFs are highly concentrated in affluent cities means that targeted measures in a few places and by selected coalitions can have a large effect covering important consumption hotspots.

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