Blog / Estimating Carbon Emissions From Income

Unconventional Carbon Credits

For the last several years I’ve been working on a project with Tega Brain to develop a series of “unconventional” carbon offsets centered around instances of industrial sabotage and direct action. The carbon credit industry is notoriously awash in bullshit, but if we were to take its premises seriously, the question arises: why do some activities get to “count” as beneficial and monetizable, but others do not? The idea of the Offset project, in short, is that when activists directly intervene in carbon flows they create carbon benefits that can be calculated and even financialized through mechanisms invented by the carbon offsetting industry itself.

For example, if an activist group blockades a coal-exporting port for a week, they are effectively forcing that port to store carbon that would have otherwise been emitted. Although this storage is temporary (sometimes very temporary) its benefit can still be calculated. Indeed, the forestry industry has developed a series of techniques, known as “ton-year accounting” for determining an equivalence ratio between temporarily storing carbon versus avoiding emissions all together. We apply these techniques to various direct actions using a methodology we’ve developed (“Industrial Sabotage as Temporary Carbon Storage”) and are thus able to quantify some forms of climate activism.

Once we have calculated the carbon reductions for a particular action, we create carbon credit certificates available for purchase in our marketplace. Proceeds from the sale of these certificates are then donated back to the activists responsible for the original action.

Footprints

While the Offset project is primarily focused on climate activism, we became curious about other forms of carbon counting, particularly around the notion of an individual’s “carbon footprint.”

The idea of a carbon footprint was widely pushed by BP to culturally shift responsibility of the climate crisis away from industry and onto the backs of individuals who were encouraged to make personal consumption or lifestyle decisions that would reduce pollution.

It seems that BP’s efforts were largely successful: most people are aware that they have something called a “carbon footprint” and that it might rise or fall depending on the type of objects and services they purchase. The metric is problematic because of how much emphasis it puts on individual rather than collective responsibility, but it also articulates an obvious insight: wealth (at the level of the individual, household, company or nation) is correlated to emissions. Some people are in fact more responsible than others.

Estimating Emissions Based on Income

Most individual carbon footprint calculators only factor in consumption. How many plane trips have you taken this year? How many car miles have you driven? And so on. And, numerous studies and papers have drawn out the relationship between individual or household consumption and emissions. On the other hand, far fewer studies have attempted to calculate emissions based income.

Is it possible to estimate individual or household emissions based on income alone, without any consideration for consumption choices?

In their paper “Income-based U.S. household carbon footprints (1990–2019) offer new insights on emissions inequality and climate finance”, Jared Starr et al. link “household-level income data to the emissions generated in creating that income.” By connecting income to emissions they’re able to show that “40% of total U.S. emissions were associated with income flows to the highest earning 10% of households,” and that the top 1% earners accounted for 15-17% of national emissions. I won’t go into their methodology here, but it’s worth a read.

While the paper does not offer a simple equation to directly express the relationship between emissions and income, the authors provide raw data containing household income amounts predicted CO2 emissions.

Here’s a simple scatter plot of pre-tax income (including benefits, investments and retirement) vs emissions using data from Starr’s article:

(NOTE: Despite the plot indicating that emissions drop to zero when income drops to zero, a well known study led by MIT Professor Timothy Gutowski has shown that “regardless of income, there is a certain floor below which the individual carbon footprint of a person in the U.S. will not drop.” This results from factoring in the “array of government services that are available to everyone in the United States” including roads, military, and other infrastructure.)

We can also plot this logarithmically (Starr et al. include the same plot in their paper):

To predict emissions from income, all we have to do is draw a line of best fit through the data points. The simplest way of doing this is pulling out the coefficients for a straight line, or a linear polynomial (that is, y = x * slope + y_intercept). It’s a one-liner with numpy:

import pandas as pd

df = pd.read_excel("./pclm.0000190.s013.xlsx")
slope, y_intercept = np.polyfit(df["DOLLARS_income_retirement_health_benefits_TOTAL"], df["CO2_income_retirement_health_benefits_TOTAL"], 1)

The results are: Emissions = Income * 0.00039185242708853076 + -1.5684975494531193.

However, there’s a hitch: Starr et al. provide two approaches to calculate income-based emissions: producer emissions and supplier emissions.

“In the producer framework, each industry’s direct operational emissions are allocated to households in proportion to the share of total income they receive from that industry. The supplier framework allocates emissions to households in the same proportional way, but each industry’s emissions are calculated as the sum of emissions occurring in all activities which directly and indirectly provide sales revenue to that industry in its role as a supplier.”

The above graphs use the supplier income method. Here’s the same scatter plot with producer emissions:

In this case, our equation becomes:

Emissions = Income * 0.0003101248414758354 + 2.855924147965089

Calculate Your Footprint

I’ve combined these estimates as upper and lower bounds to make the world’s first (??) income-based carbon footprint calculator:

(NOTE: At very low income levels, the calculator returns very low or negative emissions, which I believe, per Gutowski, is incorrect.)

A Quantitative Approach to Climate Activism

In the context of the Offset project, we are able to quantify and therefore compare the carbon benefit of various activities that previously were only evaluated qualitatively. For example, according to our calculations, the Atlanta Forest Defenders were able to offset 93,034 tons of CO2 by delaying the construction of a cop training center in Atlanta, compared to Blockade Australia, which offset 1681 tons of CO2 by shutting down a port for a week. Using our new income-based carbon calculator, we’re also able to quantify actions that aren’t typically associated with climate activism at all, like the removal of very high-income earners.

To complete the thought experiment, we have calculated the carbon benefit of UnitedHealthcare CEO Brian Thompson’s homicide, and converted that benefit into carbon credit certificates. We’ve made a conservative estimate that he would have worked for an additional 15 years before retirement, and that his reported yearly income of $10.2 million dollars would not have increased. Based on these assumptions, his killing created an avoided emissions benefit of 47,473.5 tons of CO2.

For a more in-depth piece on carbon credits, see my writing (in collaboration with Tega) in e-flux.