What City Observatory did this week

Our apologies to City Observatory readers for our website outage on 19-22 May. 

More meaningless congestion pseudo science.  A new study from the University of Maryland claims that traffic lights cause 20 percent of all time lost in traffic.  The estimate is the product of big data analysis of anonymized cell phone data, and claims that we collectively lose more than $8.6 billion per year waiting 329 million hours hours at stop lights.  The computation may be right, but in the real world is meaningless.

Once can’t speak of that time as somehow being “lost” unless there were some other alternative without traffic lights in which travel times would somehow be less.  The “loss” is purely imaginary because it would cost vastly more than the value of lost time to say, eliminate all traffic lights by building grade separated interchanges, and stop signs at intersections would likely result in even slower travel (which might improve safety).  This study is emblematic of the underlying flaw in almost all “congestion cost” studies:  they implicitly assume that there’s some way to reduce travel times–usually by widening roads–but that would cost vastly more than the value of time supposedly lost, and is ultimately futile and self-defeating because induced travel quickly erases any time savings from wider roads.

Must read

CalTrans acknowledges induced demand.  State highway departments have long been in denial about the reality of induced demand:  wider urban roads cause more travel. In a few states, DOTs are beginning to acknowledge the well-documented science of induced demand.  CalTrans has even produced a helpful non-technical diagram explaining why building more lanes is a futile and self-defeating approach to reducing traffic congestion.

The graphic was prompted by state legislation directing CalTrans to more fully consider the greenhouse gas impacts of its investments.  Perhaps they can share it with other state highway departments. (Hat tip to @EngineerDustin for flagging this graphic).

Higher registration fees for large vehicles.  Trucks and SUVs have been getting both larger and more numerous, and are particularly problematic in cities, where they pollute more and are deadlier in crashes than smaller vehicles.  While the federal tax code subsidizes larger vehicles, some cities are starting to look at levying registration fees that reflect back to large vehicle owners a portion of the cost their vehicles impose on others. David Zipper, writing at Bloomberg offers the details of a Washington, DC proposal to charge a $500 registration fee for vehicles larger than 6,000 pounds, and amount that’s about seven times higher than the standard vehicle registration fee in the district.  While local governments are generally barred from regulating vehicle characteristics, they can use fiscal incentives to discourage excessively large vehicles.  As Zipper concludes:

With traffic fatalities climbing and the effects of climate change growing more dire, passage of the District’s new fee structure could serve as a model. Even if federal officials continue to turn a blind eye, state and local leaders need not stand by while drivers of these massive vehicles impose costs on everyone else around them. Instead, they can send motorists a clear message: If you want to buy a mammoth-sized vehicle, no one is going to stop you — but you’re going to have to pay extra.

New Knowledge

The declining salience of race in emissions exposure.  Many studies have noted a strong correlations between race and income and exposure to pollution.  People of color and the poor tend to live in homes and neighborhoods with greater exposure to air pollution and toxics.  That’s true for a variety of reasons:  politically, it’s often been easier to route highways through poor neighborhoods than rich ones, and there’s usually more effective demand to eliminate or preclude pollution in wealthier neighborhoods.  Economically, neighborhoods with high levels of pollution tend to hemorrhage population:  one thing people do when they get more income is to move to places that are less polluted.

A recent study published by the National Bureau of Economic Research looks at recent trends in air pollution to see how the relationships between income, race and pollution have changed in the past couple of decades.  It draws on a kind of natural experiment, a reduction in pollution levels triggered by stricter limits on particulate pollution.  In 2005, the EPA began enforcing a new air quality standard on fine particulate matter (PM 2.5) requiring areas that violated the standard to enforce stricter pollution control requirements.  Over the next 15 years, the new standards lead to a reduction in fine particulate pollution overall, but the study showed that the reductions in pollution were greatest in neighborhoods of color (where pollution tended to be higher).  The result of the enforcement of these new standards was to both dramatically reduce overall pollution levels, and to greatly shrink the racial gap in pollution exposure.

The authors conclude:

. . .  racial differences in ambient particulate exposure declined significantly between 2000 and 2015. . . .  We focus on PM2.5 and show that the gap between African Americans and non-Hispanic Whites narrowed from -1.6 µg/m3 in 2000 to -0.5 µg/m3 by 2015. . . . We find that very little of the decline in the gap in mean exposure levels can be accounted for by changes in mobility, individual, or neighborhood-level characteristics. Similarly, we find that racial gaps in exposure have narrowed at each quantile of the PM2.5 distribution, and that little of this narrowing can be explained by the demographic characteristics available in Census data. Instead, we find that virtually all of the closure of the gap is due to falling pollution levels in the areas where African Americans are more likely to live. There is little evidence that movement of African Americans to relatively cleaner neighborhoods or non-Hispanic Whites to relatively dirtier neighborhoods has played a significant role in the observed convergence.

This is a clear example of a scientific and health based standard having substantial and positive equity effects.  The same likely applies to dealing with climate change:  because low income people and people of color are generally at greater risk from climate change than the overall population, measures that reduce greenhouse gas emissions tend to have intrinsically equitable effects.

Janet Currie, John Voorheis, and Reed Walker, What caused racial disparities in particulate exposure to fall?  New evidence from the Clean Air Act and satellite-based measures of air quality, 2020, NBER  Working Paper 26659 http://www.nber.org/papers/w26659