Koch Brothers Continue to Fund Climate Change Denial Machine, Spend $21M to Defend Exxon
The Kochs have spent more than $88 million in traceable funding to groups attacking climate change science, policy and regulation. Of that total, $21 million went to groups that recently bought a full page New York Times advertisement defending ExxonMobil from government investigations into its systematic misrepresentation of climate science.
If you're an executive at a big oil company watching as ExxonMobil is finally exposed for studying climate change, covering up the science and spreading misinformation, you're probably worried now that state attorneys general are knocking on Exxon's door.
Charles and David Koch must be worried, anyway. Their foundations gave more than $21 million to the people and groups that signed a recent, full page New York Times advertisement that defends ExxonMobil's longstanding efforts to ruin the public's understanding of climate change science.
Here Are the Numbers:
For comparison, Exxon itself spent half as much on the same people and groups, $10.1 million; money that the front groups spent on tactics like … a $100,000-or-so full page ad buy in the New York Times. (More info at Climate Investigations Center from my former colleague, Kert Davies).
The ringleader group behind the letter, the Competitive Enterprise Institute (CEI), is of particular interest. Exxon dumped CEI for its unsupportable climate stance back in 2006, a crushing blow for the aggressive beltway front group that continued to humiliate CEI staff for years.
But it appears that CEI is loyal to the cause of climate denial, despite being abandoned by Exxon a decade ago. Other financiers, like the Koch family and several coal and oil companies may explain why the denial campaign was sustained.
Traceable funds only represent a portion of the Koch family's contributions to CEI. At CEI's annual fundraising events, Koch Industries' lobbying subsidiary has been listed as a sponsor. Full-disclosure tax filings published by PR Watch revealed that Koch Industries directly paid Americans for Prosperity, the Texas Public Policy Foundation and other organizations.
PR Watch discovered another revelation in the full-disclosure tax documents that were leaked. Apparently, David Koch likes to cut CEI $100,000 checks straight from his own coffers. David Koch's money was not sent through his nonprofit foundation, which would have had to report the grants to CEI.
This incomplete patchwork of previously-undisclosed funds from Koch Industries and David Koch adds $3,124,834 to the accounting on groups that co-signed the CEI ad. This raises the question: who else is just cutting a direct check to the climate deniers?
And then there's the “Dark Money ATM" sister groups, DonorsTrust and Donors Capital Fund. The DonorsTrust franchise is run by CEI's former president, Lawson Bader, who helps donors—including Koch—anonymize tens of millions of dollars that go to dozens of front groups each year. DonorsTrust & Capital Fund have funneled millions of dark money dollars to CEI.
But that's still not the end of the financial trail. Other mechanisms used by Charles Koch and his army of donor friends include Freedom Partners Chamber of Commerce, a dark money umbrella group that has hidden hundreds of millions of dollars in politically-charged cash, shuffled between various trusts, nonprofits and limited liability corporations.
For the deep history, check out Kert Davies' post for the Climate Investigations Center, which spurred my own interest in the sponsors of the recent New York Times ad. Kert details the crucial history of some of the letter's signatories, the role they have served in the climate denial machine over the years and the exact documents that inform his understanding.
I have reproduced Kert's ExxonSecrets map (below) of the players involved, as it helps show how a small group of people funded by a few oil and coal companies can cast a shadow that is deceptively deep. The tobacco industry crafted this deceptive model and fossil fuel companies have innovated it since. It helps that the same people doing tobacco science denial moved on to climate science denial.
One of those tobacco denial alumni, lawyer Steve Milloy, himself an aggressive defender of ExxonMobil, knows that a small group of people can have an outsized impact with enough funding—even in the face of 97-99 percent of the world's climate scientists. Milloy once said, “There's really only about 25 of us doing this. A core group of skeptics. It's a ragtag bunch, very Continental Army."
This indicates that folks like Milloy aren't just deceiving the public, but themselves. If I was taking Charles Koch's money to attack science, I too would probably have to constantly remind myself of my American heroics.
Mr. Koch is as awkward as ever in his half-hearted attempts to understand climate change science (you'd think a MIT alumnus would get it), he has been wary of climate laws and regulations for a long time.
That's probably why he has rained cash on the organizations that stage the fight, groups that have given room for a top U.S. CEO, with a background in chemical engineering, to demonstrate such scientific ignorance. Since 1997, the Kochs have spent more than $88 million in traceable dollars into the network of groups that attack climate science, the scientists doing the research, the potential policy solutions and the champions of those policies.
ExxonSecrets Map of the Players:
Connor Gibson does research for Greenpeace's Investigations team. He focuses on polluting industries, their front groups and PR operatives, particularly focusing on the Koch Brothers.
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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>
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