For a better browsing experience, please upgrade your browser.

FiveThirtyEight

Politics

Regional considerations tend to loom larger in debates over environmental policy than in other sorts of affairs. Some states consume more energy than others. Some states have more carbon-intensive economies than others. Some states are more or less likely to be negatively impacted by global warming. And some states are better equipped to take advantage of green energy development.

Today, I’m going to focus on the first of those concerns: household energy usage. The goal here is simple: the Congressional Budget Office recently put out an estimate (.pdf) of the costs of the Waxman-Markey cap-and-trade bill. The CBO estimated that the average American household would wind up paying a net of $175 in additional energy costs in the year it benchmarked, which was 2020. But how does that cost translate to individual states?

First, here’s the map, and then I’ll explain how I arrived at these numbers:

(large version) (color-blind version)

Before I go any further, let me make clear that my objective is to translate the CBO’s numbers, using my best interpretation of the CBO’s assumptions, to the level of individual states. I don’t make any other sort of judgment about the reliability of their numbers. If you don’t like the CBO’s numbers, you won’t like mine.

There are two principal drivers of the differences in costs between different states. One driver is the amount of carbon that residential customers in each state use, and the other is its income distribution. The reason the latter is important is because Waxman-Markey offers a series of direct and indirect subsidies to taxpayers that are intended to offset the increased energy costs, and some of those subsidies are targeted based on the income of the taxpayer.

But first, the more straightforward issue, which is carbon consumption. These numbers are taken from the EPA’s most recent (2005) estimates (.pdf) of the amount of CO2 emissions released in each state. The EPA breaks these down into five categories: residential, commercial, industrial, transportation, and electric power. We are concerned with two of those categories: residential and transportation.

Direct, residential use of carbon, such as for home heating fuel, is actually a relatively small part of the carbon picture, accounting for 5-6 percent of domestic carbon consumption. As you can see, the rule here is pretty simple — it evidently takes more carbon to heat your home than to cool it, and so colder states are associated with more residential carbon usage per capita, with the exception of a few states in the Pacific Northwest.

Most carbon consumption in the transportation sector — about 60 percent — is the result of the usage of personal cars, and is therefore paid for directly by taxpayers in the form of gasoline prices. Some states, particularly Southern states, do more driving than others; there are also differences related to fuel efficiency standards, the availability of public transportation (such as in New York) and so forth. A couple of states — namely Wyoming and Alaska — are extreme outliers owing to what I believe is the relatively high usage of personal aircraft. Note that transportation constitutes a much bigger piece of the carbon puzzle than do home energy costs — about 30 percent of all U.S. emissions.

To estimate the amount of residential carbon usage in each state, I take the EPA’s CO2 estimate for the residential sector and add it to 60 percent of their estimate for the transportation sector, then divide the result by the number of households in each state. What about the other sources of carbon emissions, like industrial use and electricity production? They are certainly relevant insofar as the regional impacts of Waxman-Markey go, but they are not relevant in terms of interpreting the CBO’s estimates, which seek to determine the direct cost to taxpayers in the form of higher energy prices only. For instance, West Virginia is associated with high carbon consumption in its commercial sector because of its production of coal. But much of that coal is exported to other states; the amount of carbon that residential customers in West Virginia consume is not particularly high. That does not mean that West Virginians don’t have reason to fret about Waxman-Markey — it’s just a different type of cost than we’re trying to get at here. Conversely, some states like Maine which have high residential use of carbon do not have particularly carbon-intensive economies.

The other major factor is the income distribution in each state. Under Waxman-Markey, the CBO estimates, people in the lowest income quintile will get 94 percent of their marginal costs back in the form of direct consumer rebates, whereas people in the top income quintile will get 18 percent back, with the other quintiles scaling accordingly. These types of benefits, in other words, are directly proportionate to carbon consumption. There are also indirect forms of subsidy, in the form of offsets offered to carbon producers that will “trickle down” to the household level. I assume that these indirect subsidies are unrelated to carbon consumption and are solely determined by a state’s income distribution.

Let’s get a bit more specific. The CBO estimates nationwide costs and benefits from the cap-and-trade program to be as follows:

Now, how do we translate these numbers to individual states? There are four relatively simple steps:

Step 1. Scale the gross costs to each state’s income-adjusted carbon usage. Minnesota, which we’ll use as our example, uses 15.1 million metric tons of residential carbon per household, which represents 108 percent of the national average. So, do we simply multiply the gross costs in Minnesota by 1.08? Unfortunately, it’s not quite that easy, because per the CBO’s method, we’re trying to estimate the carbon costs for particular income quintiles in each state, and not simply the overall number. Minnesota uses more carbon than average, but it’s also wealthier than average, and wealthy people use more carbon, all else being equal. Thus, we have to scale each state’s carbon usage to its income distribution to avoid what amounts to double-counting. I won’t go into details, but this lowers Minnesota’s income-adjusted carbon usage to 104 percent of the national average. Therefore, I multiply the gross costs from the CBO’s national estimates by 1.04 to cater them to Minnesota.

Step 2. Account for rebates. As mentioned previously, I assume that the direct rebates are proportionate to the cost of carbon consumption for each income quintile in each state. Consumers in the lowest income quintile get 94 percent of their costs back, scaling downward to 76 percent, 44 percent, 33 percent and 18 percent as we move up the income pyramid. Conversely, I assume that the indirect subsidies are not proportionate to carbon usage and are the same in each state. In other words, I simply plug in the CBO’s numbers for these.

Step 3. Subtract the rebates from the gross costs for each income quintile. This is trivial.

Step 4. Take a weighted average of the net costs for each state based on its income distribution. This is also straightforward. If 30 percent of a state’s residents fall into the lowest income quintile (relative to the entire country), we multiply the net cost estimate for the lowest quintile in that state by 30 percent, repeating this process for the other quintiles to create a weighted average.

Here, then, is our estimate of the per-household cost of cap-and-trade for Minnesota:

We estimate that the average cost per household in Minnesota per the CBO’s assumptions is $202, which is slightly higher than the national average of $175 owing to the state’s slightly higher-than-average residential carbon consumption and its slightly higher-than-average incomes.

I realize that those last four or five paragraphs are probably just about the most boring thing you’ve ever read on FiveThirtyEight but sometimes you have to show your work. In any event, here are our estimates for all 50 states plus the District of Columbia:

And here’s that map again:

There is a fair amount of state-to-state variance, although it is exaggerated somewhat by the presence of a couple of outliers: Florida and D.C. on the one side and Wyoming and Alaska, which I think are being punished for the use of personal jet travel, on the other. The key question for the bill’s passage might be whether Democrats can pick up some Republican votes in large, coastal states like Florida, California, New York, and North Carolina, each of which appears to be associated with below-average costs to end-users. Conversely, most of the places with the highest direct costs are places where the Democrats weren’t likely to pick up many votes anyway, although this does suggest that votes like Mark Begich’s in Alaska and Mary Landireu’s in Louisiana will be tough ones if this gets to the Senate.

Filed under , ,

Comments Add Comment

Never miss the best of FiveThirtyEight.

Subscribe to the FiveThirtyEight Newsletter
×

Sign up for our newsletters to keep up with our favorite articles, charts and regressions. We have two on offer: The first is a curated digest of the best of what’s run on FiveThirtyEight that week. The other is Ctrl + , our weekly look at the best data journalism from around the web. Enter your email below, and we’ll be in touch.




By clicking subscribe, you agree to the FanBridge Privacy Policy

Powered by WordPress.com VIP