In the frenzy of coverage of the outrageous Volkswagen use of sophisticated computer techniques to shut down the pollution controls on 11 million of VW’s TDI diesel automobiles once they were no longer sitting in a test facility, only a few have noted that this is not the first such episode—although the last one involved mostly American companies, not German and trucks, not cars.
After the VW scandal, people don't trust big companies http://t.co/3S5c96WHn5 http://t.co/qod9c4xBUq— The Independent (@The Independent)1443330303.0
The current scandal has cost VW 1/3 of its market cap, its CEO his job and delivered a potentially fatal blow to the market for “clean diesel” cars because, it turns out, VW wasn’t producing “clean diesel”—it was cheating the emissions tests. (Full disclosure: when the TDI diesels first appeared they received “Green Car of the Year” status from a panel on which I served. I don’t recall my vote, alas, but I certainly took VW’s emissions claims at face value.)
By marketing “clean” TDI diesels, VW avoided the investments required to add electric or hydrogen vehicles to its fleets and clung to the internal combustion technology of the past. Now, with clean diesel exposed as an oxymoron, VW has an enormous challenge—how to catch up with arch rivals like Toyota which led on hybrids and electrification, while incurring potential enormous fines and a savaged brand value.
Not only VW, but the reputation of Germany and its governments (which own much of the auto industry) have been severely damaged: “playing by the rules” has been the moral stick Germany wielded to assert insist on austerity, not debt relief, as the solution to the problems of Euro members like Greece.
Now one of Germany’s premier companies has been shown to be a massive cheater. But it didn’t invent the ploy. VW used a more sophisticated, but fundamentally identical strategy to that which a U.S. diesel truck engine makers deployed in the 1990’s, eventually producing 1.3 million diesel trucks programmed to turn off their emission control devices after the first 50 miles of freeway driving.
The computer shenanigans engineered by Volkswagen were more elaborate—they turn off the pollution controls even in city driving, because cars operate far more of the time at low speeds and because on-board computers now control engines in far more sophisticated ways. But the core strategies and the business goal, were identical.
And the reason VW resorted to cheating was the same reason that U.S. truck manufacturers cheated in the 1990’s—controlling the nitrogen pollution from diesel engines is possible, but comes either at a substantial price for expensive, cumbersome pollution controls or demands a fuel economy penalty, shortens engine life and degrades performance. So to sell small, cheap diesels, you have to sell polluting ones—it’s an engineering reality.
Volkswagen had to know the history of the “defeat devices” used by the U.S. diesel truck makers in the 1990’s—and that means they had to know of the complete illegality of what they were doing. In the 1990’s case engine manufacturers claimed that U.S. Environmental Protection Agency (EPA) had given them permission to turn off its pollution control equipment on the highway—although they still signed a consent decree and paid a billion dollars in fines and correctional programs. But they never explained why they thought that their behavior was in the public interest or why EPA would have approved it. They clearly just didn’t like the pollution standards.
But in spite of this clear bright line history, VW simply chose to take the chance that regulators would never catch it. And one of the reasons, almost certainly, is the way that EPA chose to deal with the diesel engine manufacturers in 1998. While the settlement, at $1 billion, was trumpeted by EPA as the biggest pollution fine ever, most of the money was simply to correct the pollution violations by rebuilding the truck engines as they came in for periodic maintenance. Very little were actual fines. At the time, the Sierra Club and I objected that EPA was not requiring a recall of the engines, but the agency maintained that the routine rebuild—plus the acceleration of the effective date of the next generation of diesel truck nitrogen standards—would clean up the air more effectively than penalties designed to punish the engine makers.
Four years later, as the ostensibly “welcome” new emission standards kicked in, EPA officials were forced to investigate whether Caterpillar, whose engines still didn’t meet the new standards, was illegally encouraging its customers to “pre-order” high emitting truck engines before the new standards took effect. So the biggest of the diesel manufacturers showed little sign of remorse—they simply looked for other ways to game the system.
The VW scandal has unleashed a tidal wave of media coverage of the reality that auto industry has systematically tried to game and cheat both safety and emission regulations since they first came in—and that governments have (with the notable exception of California) largely collaborated with the evasions. The Economist goes as far as to argue that this scandal reveals the need for—and may enable—a very different kind of auto industry. UBS analysts suggested that the credibility problems might accelerate the end of all forms of internal combustion transportation—not just passenger diesel.
It’s pretty clear that whether the issue is safety or pollution, major manufacturers don’t take cheating on government standards as seriously as they would, say, take theft. The well established culture is “if the likely financial benefits exceed the probable penalties, cheat.” And this runs deep in the business world—it’s not just VW or even the auto industry.
What would deter cheating? (Absent the certainty of detection and penalties which is an improbably difficult hurdle). One thing: the belief that not only will the bottom line take a hit, but the careers and reputations and perhaps liberty of those involved will be at risk. That’s what deters thieves—you might go to jail, not just have to return your stolen goods. In the wake of the GM safety settlement, the Department of Justice has promised that in the future, these kinds of penalties will be used. And that’s what’s needed to deter obvious, intentional and willful cheating on pollution and safety standards, cheating which, let’s remember, kills.
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By Eric Tate and Christopher Emrich
Disasters stemming from hazards like floods, wildfires, and disease often garner attention because of their extreme conditions and heavy societal impacts. Although the nature of the damage may vary, major disasters are alike in that socially vulnerable populations often experience the worst repercussions. For example, we saw this following Hurricanes Katrina and Harvey, each of which generated widespread physical damage and outsized impacts to low-income and minority survivors.
Mapping Social Vulnerability<p>Figure 1a is a typical map of social vulnerability across the United States at the census tract level based on the Social Vulnerability Index (SoVI) algorithm of <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/1540-6237.8402002" target="_blank"><em>Cutter et al.</em></a> . Spatial representation of the index depicts high social vulnerability regionally in the Southwest, upper Great Plains, eastern Oklahoma, southern Texas, and southern Appalachia, among other places. With such a map, users can focus attention on select places and identify population characteristics associated with elevated vulnerabilities.</p>
Fig. 1. (a) Social vulnerability across the United States at the census tract scale is mapped here following the Social Vulnerability Index (SoVI). Red and pink hues indicate high social vulnerability. (b) This bivariate map depicts social vulnerability (blue hues) and annualized per capita hazard losses (pink hues) for U.S. counties from 2010 to 2019.<p>Many current indexes in the United States and abroad are direct or conceptual offshoots of SoVI, which has been widely replicated [e.g., <a href="https://link.springer.com/article/10.1007/s13753-016-0090-9" target="_blank"><em>de Loyola Hummell et al.</em></a>, 2016]. The U.S. Centers for Disease Control and Prevention (CDC) <a href="https://www.atsdr.cdc.gov/placeandhealth/svi/index.html" target="_blank">has also developed</a> a commonly used social vulnerability index intended to help local officials identify communities that may need support before, during, and after disasters.</p><p>The first modeling and mapping efforts, starting around the mid-2000s, largely focused on describing spatial distributions of social vulnerability at varying geographic scales. Over time, research in this area came to emphasize spatial comparisons between social vulnerability and physical hazards [<a href="https://doi.org/10.1007/s11069-009-9376-1" target="_blank"><em>Wood et al.</em></a>, 2010], modeling population dynamics following disasters [<a href="https://link.springer.com/article/10.1007%2Fs11111-008-0072-y" target="_blank" rel="noopener noreferrer"><em>Myers et al.</em></a>, 2008], and quantifying the robustness of social vulnerability measures [<a href="https://doi.org/10.1007/s11069-012-0152-2" target="_blank" rel="noopener noreferrer"><em>Tate</em></a>, 2012].</p><p>More recent work is beginning to dissolve barriers between social vulnerability and environmental justice scholarship [<a href="https://doi.org/10.2105/AJPH.2018.304846" target="_blank" rel="noopener noreferrer"><em>Chakraborty et al.</em></a>, 2019], which has traditionally focused on root causes of exposure to pollution hazards. Another prominent new research direction involves deeper interrogation of social vulnerability drivers in specific hazard contexts and disaster phases (e.g., before, during, after). Such work has revealed that interactions among drivers are important, but existing case studies are ill suited to guiding development of new indicators [<a href="https://doi.org/10.1016/j.ijdrr.2015.09.013" target="_blank" rel="noopener noreferrer"><em>Rufat et al.</em></a>, 2015].</p><p>Advances in geostatistical analyses have enabled researchers to characterize interactions more accurately among social vulnerability and hazard outcomes. Figure 1b depicts social vulnerability and annualized per capita hazard losses for U.S. counties from 2010 to 2019, facilitating visualization of the spatial coincidence of pre‑event susceptibilities and hazard impacts. Places ranked high in both dimensions may be priority locations for management interventions. Further, such analysis provides invaluable comparisons between places as well as information summarizing state and regional conditions.</p><p>In Figure 2, we take the analysis of interactions a step further, dividing counties into two categories: those experiencing annual per capita losses above or below the national average from 2010 to 2019. The differences among individual race, ethnicity, and poverty variables between the two county groups are small. But expressing race together with poverty (poverty attenuated by race) produces quite different results: Counties with high hazard losses have higher percentages of both impoverished Black populations and impoverished white populations than counties with low hazard losses. These county differences are most pronounced for impoverished Black populations.</p>
Fig. 2. Differences in population percentages between counties experiencing annual per capita losses above or below the national average from 2010 to 2019 for individual and compound social vulnerability indicators (race and poverty).<p>Our current work focuses on social vulnerability to floods using geostatistical modeling and mapping. The research directions are twofold. The first is to develop hazard-specific indicators of social vulnerability to aid in mitigation planning [<a href="https://doi.org/10.1007/s11069-020-04470-2" target="_blank" rel="noopener noreferrer"><em>Tate et al.</em></a>, 2021]. Because natural hazards differ in their innate characteristics (e.g., rate of onset, spatial extent), causal processes (e.g., urbanization, meteorology), and programmatic responses by government, manifestations of social vulnerability vary across hazards.</p><p>The second is to assess the degree to which socially vulnerable populations benefit from the leading disaster recovery programs [<a href="https://doi.org/10.1080/17477891.2019.1675578" target="_blank" rel="noopener noreferrer"><em>Emrich et al.</em></a>, 2020], such as the Federal Emergency Management Agency's (FEMA) <a href="https://www.fema.gov/individual-disaster-assistance" target="_blank" rel="noopener noreferrer">Individual Assistance</a> program and the U.S. Department of Housing and Urban Development's Community Development Block Grant (CDBG) <a href="https://www.hudexchange.info/programs/cdbg-dr/" target="_blank" rel="noopener noreferrer">Disaster Recovery</a> program. Both research directions posit social vulnerability indicators as potential measures of social equity.</p>
Social Vulnerability as a Measure of Equity<p>Given their focus on social marginalization and economic barriers, social vulnerability indicators are attracting growing scientific interest as measures of inequity resulting from disasters. Indeed, social vulnerability and inequity are related concepts. Social vulnerability research explores the differential susceptibilities and capacities of disaster-affected populations, whereas social equity analyses tend to focus on population disparities in the allocation of resources for hazard mitigation and disaster recovery. Interventions with an equity focus emphasize full and equal resource access for all people with unmet disaster needs.</p><p>Yet newer studies of inequity in disaster programs have documented troubling disparities in income, race, and home ownership among those who <a href="https://eos.org/articles/equity-concerns-raised-in-federal-flood-property-buyouts" target="_blank">participate in flood buyout programs</a>, are <a href="https://www.eenews.net/stories/1063477407" target="_blank" rel="noopener noreferrer">eligible for postdisaster loans</a>, receive short-term recovery assistance [<a href="https://doi.org/10.1016/j.ijdrr.2020.102010" target="_blank" rel="noopener noreferrer"><em>Drakes et al.</em></a>, 2021], and have <a href="https://www.texastribune.org/2020/08/25/texas-natural-disasters--mental-health/" target="_blank" rel="noopener noreferrer">access to mental health services</a>. For example, a recent analysis of federal flood buyouts found racial privilege to be infused at multiple program stages and geographic scales, resulting in resources that disproportionately benefit whiter and more urban counties and neighborhoods [<a href="https://doi.org/10.1177/2378023120905439" target="_blank" rel="noopener noreferrer"><em>Elliott et al.</em></a>, 2020].</p><p>Investments in disaster risk reduction are largely prioritized on the basis of hazard modeling, historical impacts, and economic risk. Social equity, meanwhile, has been far less integrated into the considerations of public agencies for hazard and disaster management. But this situation may be beginning to shift. Following the adage of "what gets measured gets managed," social equity metrics are increasingly being inserted into disaster management.</p><p>At the national level, FEMA has <a href="https://www.fema.gov/news-release/20200220/fema-releases-affordability-framework-national-flood-insurance-program" target="_blank">developed options</a> to increase the affordability of flood insurance [Federal Emergency Management Agency, 2018]. At the subnational scale, Puerto Rico has integrated social vulnerability into its CDBG Mitigation Action Plan, expanding its considerations of risk beyond only economic factors. At the local level, Harris County, Texas, has begun using social vulnerability indicators alongside traditional measures of flood risk to introduce equity into the prioritization of flood mitigation projects [<a href="https://www.hcfcd.org/Portals/62/Resilience/Bond-Program/Prioritization-Framework/final_prioritization-framework-report_20190827.pdf?ver=2019-09-19-092535-743" target="_blank" rel="noopener noreferrer"><em>Harris County Flood Control District</em></a>, 2019].</p><p>Unfortunately, many existing measures of disaster equity fall short. They may be unidimensional, using single indicators such as income in places where underlying vulnerability processes suggest that a multidimensional measure like racialized poverty (Figure 2) would be more valid. And criteria presumed to be objective and neutral for determining resource allocation, such as economic loss and cost-benefit ratios, prioritize asset value over social equity. For example, following the <a href="http://www.cedar-rapids.org/discover_cedar_rapids/flood_of_2008/2008_flood_facts.php" target="_blank" rel="noopener noreferrer">2008 flooding</a> in Cedar Rapids, Iowa, cost-benefit criteria supported new flood protections for the city's central business district on the east side of the Cedar River but not for vulnerable populations and workforce housing on the west side.</p><p>Furthermore, many equity measures are aspatial or ahistorical, even though the roots of marginalization may lie in systemic and spatially explicit processes that originated long ago like redlining and urban renewal. More research is thus needed to understand which measures are most suitable for which social equity analyses.</p>
Challenges for Disaster Equity Analysis<p>Across studies that quantify, map, and analyze social vulnerability to natural hazards, modelers have faced recurrent measurement challenges, many of which also apply in measuring disaster equity (Table 1). The first is clearly establishing the purpose of an equity analysis by defining characteristics such as the end user and intended use, the type of hazard, and the disaster stage (i.e., mitigation, response, or recovery). Analyses using generalized indicators like the CDC Social Vulnerability Index may be appropriate for identifying broad areas of concern, whereas more detailed analyses are ideal for high-stakes decisions about budget allocations and project prioritization.</p>
By Jessica Corbett
Sen. Bernie Sanders on Tuesday was the lone progressive to vote against Tom Vilsack reprising his role as secretary of agriculture, citing concerns that progressive advocacy groups have been raising since even before President Joe Biden officially nominated the former Obama administration appointee.