Racial inequity in who pollutes and who gets exposed to pollution

A recent paper led by Chris Tessum (University of Washington) and published in PNAS brings novel estimates of racial-ethnic disparities in air pollution cause and exposure in the US. They find that air pollution is disproportionately caused by consumption by white Americans, but disproportionately affects Black and Hispanic Americans. The air pollution input-output model used in the paper is freely available and there's an experimental live version of the model running, here. (thanks Marko Tainio for pointing to this paper)

Abstract

Fine particulate matter (PM2.5) air pollution exposure is the largest environmental health risk factor in the United States. Here, we link PM2.5 exposure to the human activities responsible for PM2.5 pollution. We use these results to explore “pollution inequity”: the difference between the environmental health damage caused by a racial–ethnic group and the damage that group experiences. We show that, in the United States, PM2.5 exposure is disproportionately caused by consumption of goods and services mainly by the non-Hispanic white majority, but disproportionately inhaled by black and Hispanic minorities. On average, non-Hispanic whites experience a “pollution advantage”: They experience ∼17% less air pollution exposure than is caused by their consumption. Blacks and Hispanics on average bear a “pollution burden” of 56% and 63% excess exposure, respectively, relative to the exposure caused by their consumption. The total disparity is caused as much by how much people consume as by how much pollution they breathe. Differences in the types of goods and services consumed by each group are less important. PM2.5 exposures declined ∼50% during 2002–2015 for all three racial–ethnic groups, but pollution inequity has remained high.

credit: Tessum et a..

Globally consistent estimate of carbon footprints of 189 countries and 13,000 cities

Daniel D Moran et colleagues developed the Global Gridded Model of Carbon Footprints (GGMCF). This model provides a globally consistent and spatially resolved (250m) estimate of carbon footprints in per capita and absolute terms across 189 countries. Their paper got recently accepted for publication (see below) and their data is freely available. Kudos to the team!

Moran, D., Kanemoto, K., Jiborn, M., Wood, R., Többen, J., & Seto, K. (2018). Carbon footprints of 13,000 cities. Environmental Research Letters.

Abstract:

While it is understood that cities generate the majority of carbon emissions, for most cities, towns, and rural areas around the world no carbon footprint (CF) has been estimated. The Gridded Global Model of City Footprints (GGMCF) presented here downscales national CFs into a 250m gridded model using data on population, purchasing power, and existing subnational CF studies from the US, China, EU, and Japan. Studies have shown that CFs are highly concentrated by income, with the top decile of earners driving 30-45% of emissions. Even allowing for significant modeling uncertainties, we find that emissions are similarly concentrated in a small number of cities. The highest emitting 100 urban areas (defined as contiguous population clusters) account for 18% of the global carbon footprint. While many of the cities with the highest footprints are in countries with high carbon footprints, nearly one quarter of the top cities (41 of the top 200) are in countries with relatively low emissions. In these cities population and affluence combine to drive footprints at a scale similar to those of cities in high-income countries. We conclude that concerted action by a limited number of local governments can have a disproportionate impact on global emissions.

credit: Moran et al

Assorted Links

1. Thanks to Geoff Boeing's great work, you can easily create isochrone maps for anywhere in the world automatically with Python and its OSMnx package
   
   
2. Christian Brand (TSU/Oxford) is one of the co-authors of a new WHO report on "Health economic assessment tools (HEAT) for walking and for cycling"
   
   
3. An AI experiment to find unsafe housing using street-level images, by Jonathan Jay
   
   
4. The Right to the City: Free Ebook Download
   
   
5. Rio's mega-event transportation investments did not address inequalities (or even improve mobility)
   
   
6. call for papers on supply chain and logistics:  2018 MIT SCALE Latin America Conference
   
   
7. The Harvard University Data Science Initiative is seeking applications for its Harvard Data Science Postdoctoral Fellows Program for the 2018-2019 academic year
   
   
8. Google has been mapping air pollution using Google Street View cars. So far, they've measured over one billion air quality data points and now air quality scientists can request access to their data