Addressing, and even measuring, EJ is harder than it looks.
Lots of people are talking about environmental injustice – from the Flint drinking water crisis to claims that cap-and-trade policies in California increased pollution near poor, minority neighborhoods. These are important issues, so it’s crucial to understand what’s going on and implement the right policies.
I recently read a fantastic review of the economic research on environmental justice (EJ). It’s a paper by Banzhaf, Ma and Timmins published in an economics journal that’s designed to be accessible (it is, really!), so I highly recommend it. Here are a couple of the authors’ points.
Measurement is Hard
You might think it’s easy to determine that there’s systematic environmental injustice: just measure whether non-white and low-income communities are exposed to more air, water and other forms of pollution than richer, whiter communities. But, when you go to the data, it gets messy. For example, are you trying to correlate pollution exposure and racial composition at the county level or by smaller neighborhoods?
Banzhaf and co-authors (I’ll refer to them as BMT for short) point out that these seemingly innocuous measurement decisions can matter. For example, in the schematic below, there’s a clear pattern of environmental injustice – the yellow triangle pollution sources are only in the blue squares, meant to identify neighborhoods where non-whites are in the majority. If researchers have access to neighborhood-level data, they would detect the environmental injustice.
But, if researchers have access to data with less geographic resolution, say the areas defined by the black squares on the right, the data would suggest no relationship between demographics and pollution – every black square has the same number of pollution sources and the same mix of white and non-white neighborhoods. Getting finer grained pollution measurements can be difficult, though, as Meredith’s recent post pointed out.
And, finer grained measurement won’t necessarily identify more environmental injustice. For example, some fine-grained spatial measurements are designed to be homogenous – think of gerrymandered congressional districts. If pollution disperses equally from a source located in a neighborhood dominated by lower income households, but the researcher thinks the pollution follows the exact contours of the gerrymandered neighborhood, measurements based on the fine-grained neighborhood could overstate the number of low-income people who are exposed.
In practice, BMT conclude that as researchers have come across more geographically precise data sets, they have generally found more dramatic correlations between local pollution and low-income and non-white communities.
Solutions Can Backfire
Now imagine you have overcome the measurement issues and identified a clear example of environmental injustice – say a polluting plant in a predominantly low-income neighborhood. And, even better, you’ve persuaded the local policymaker to do something about it. To continue the daydream, imagine that the policymaker succeeds in not only convincing the plant to reduce pollution, but also creating a local green space right next to the now-clean plant. Sounds great, right? Victory for the EJ advocates.
Not so fast. Because, before you know it, wealthier people figure out that this is now a nice neighborhood with a great park, and a couple of them move in. Starbucks and Soul Cycle figure out that there are wealthy people in the neighborhood, so they open local franchises, and more wealthy people, who like to live within walking distance of coffee and expensive exercise classes, move in. Eventually, you’ve got what BMT call environmental gentrification. The poor people who the well-intentioned policymaker set out to help are priced out of their now-expensive rental units, and their wealthy, absentee landlords are happily cashing larger rent checks.
These darn market forces are powerful.
Attribution is Tricky
In our example, the policymaker were able to pinpoint the polluting plant in the low-income neighborhood and do something about it. But, BMT also point out that figuring out the root cause of the environmental injustice can be difficult. Which policies should we try to change to address it?
Here in California, that difficulty is encapsulated in the debate about the impact of our landmark cap-and-trade program on environmental justice. A 2016 report from the USC Program for Environmental and Regional Equity (PERE) received a lot of attention for concluding that pollution from many CA plants located in disadvantaged communities went up after they were subject to cap-and-trade. The report was subsequently published here.
But, the PERE report confuses correlation with causation. A number of other things also changed in California right around the time that the cap-and-trade program started. For example, the San Onofre Nuclear Generating Station (SONGS) closed despite cap-and-trade, which made it marginally more profitable. Replacing the electricity that a 2,000+ MW nuclear plant had produced played a big role in driving up pollution from fossil fuel plants in California. (Meredith’s blog post elegantly discusses the critiques of the PERE report and Lucas and Catie Hausman’s paper documents the impacts of the nuclear plant closure on fossil-fuel generation in California. Also, a paper by Kyle Meng at UC Santa Barbara finds that cap-and-trade did not lead to more pollution in disadvantaged communities in California.) Unfortunately, I’ve run into a number of people who are convinced that cap-and-trade is bad for environmental justice no matter what.
BMT conclude with a recommendation that may seem surprising from economists – it’s not some perfectly designed tax. Instead, they suggest, “giving local populations a seat at the table when making decisions that affect the local environment.” Political institutions could matter, but will they matter enough? For example, California recently set up the Disadvantaged Communities Advisory Group to “review and advise” on the environmental justice implications of energy regulations. Let’s hope the group is impactful.
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Suggested citation for this blog post:
Wolfram, Catherine. “An Economic Perspective on Environmental Justice” Energy Institute Blog, UC Berkeley, May 6, 2019, https://energyathaas.wordpress.com/2019/05/06/an-economic-perspective-on-environmental-justice/
Catherine Wolfram is the Cora Jane Flood Professor of Business Administration at the Haas School of Business, University of California, Berkeley. During Academic year 2018-19, she will serve as the Acting Associate Dean for Academic Affairs at Berkeley Haas. She is the Program Director of the National Bureau of Economic Research's Environment and Energy Economics Program, Faculty Director of The E2e Project, a research organization focused on energy efficiency and a research affiliate at the Energy Institute at Haas. She is also an affiliated faculty member of in the Agriculture and Resource Economics department and the Energy and Resources Group at Berkeley.
Wolfram has published extensively on the economics of energy markets. Her work has analyzed rural electrification programs in the developing world, energy efficiency programs in the US, the effects of environmental regulation on energy markets and the impact of privatization and restructuring in the US and UK. She is currently implementing several randomized controlled trials to evaluate energy programs in the U.S., Ghana, and Kenya.
She received a PhD in Economics from MIT in 1996 and an AB from Harvard in 1989. Before joining the faculty at UC Berkeley, she was an Assistant Professor of Economics at Harvard.