Southern States Need to Divest From Coal and Invest in Renewable Energy

By Angela Garrone
Alabama
Alabama relies heavily on coal-fired power, with 40 percent of the state’s energy in 2013 coming from coal. Despite many misconceptions, however, most of the coal burned in Alabama came from outside the state and cost a significant amount of money. In 2012, Alabama imported 18.5 million tons of coal from six U.S. states and Colombia, which accounted for 75 percent of the coal burned in Alabama coal plants. Due to this significant amount of imported coal, Alabama ranked eighth nationally for money spent on net coal imports and first in the nation for expenditures on international coal imports. The state’s largest power provider, Alabama Power, sent $710 million out of state to purchase coal and Alabama Power’s parent company, Southern Company, ranks first among all U.S. power providers for coal import dependency.
Energy efficiency is one of the quickest and most affordable ways to cut coal-fired power while boosting the local economy. Yet Alabama’s energy efficiency potential remains largely untapped. The state budgeted just $2.09 per person on ratepayer-funded electricity efficiency programs—101 times less than Alabama utilities spent on imported coal. Thanks to reductions in wind costs and recent advances in low-wind speed technology, several wind projects are underway in Alabama. Alabama Power purchased 404 megawatts (MW) of wind power from Kansas and Oklahoma in 2012. We are hopeful that Alabama Power will continue to increase investments in wind and solar power and decrease its reliance on dirty, coal-fired energy.
Tennessee
Although Tennessee has reduced its reliance on coal, it continued to rely on coal to provide almost half of its energy in 2013. More than 99 percent of the coal burned in Tennessee is imported from eight other states across the country. In 2012, the state spent a net total of $905 million to import 18.4 million tons of coal—making Tennessee the ninth highest state nationally in terms of coal import dependency. The Tennessee Valley Authority, which produces electricity for the vast majority Tennesseans, ranks third among U.S. power providers for coal import dependency, spending nearly $1.4 billion in 2012 on out-of-state coal across its holdings in Tennessee, Alabama and Kentucky.
In a recent TVA Board meeting, CEO Bill Johnson laid out an aspirational goal of moving to a more balanced resource portfolio that would include only 20 percent reliance on coal-fired power. Tennessee’s energy efficiency resources are largely untapped—ranking thirty-first nationally for state achieved energy efficiency savings. As TVA continues its 2015 IRP planning process, we are hopeful that TVA will continue to reduce its reliance on coal-fired energy and increase its in-state renewable and energy efficiency resources.
Georgia
Georgia relied on coal to provide around 39 percent of its energy in 2013. Power producers in Georgia paid nearly $1.7 billion to import 23.4 million tons of coal—primarily from Kentucky and Wyoming. Georgia ranks third highest in the nation for money spent on net coal imports. This high ranking is not surprising considering that Georgia Power, Georgia’s largest power provider, is also a subsidiary of the top-ranking importer, Southern Company.
Georgia is not in the top ranks, however, among states with significant energy efficiency savings—coming in forty-second in the nation with 0.11 percent savings in 2011. Although Georgia has a wealth of renewable energy resources like solar and wind, unfortunately only 2.3 percent of Georgia’s energy was generated by renewable resources in 2012. In its most recent Integrated Resource Plan (IRP), Georgia Power set out a plan to procure 735 MW of solar energy by 2016. We support Georgia Power’s recent commitments to retire coal plants as well as its investments in the clean energy economy; we look forward to the growth of renewable energy across the state.
Florida
Florida relied on coal-fired power for around 20 percent of its total energy in 2013. Like many Southern states, Florida has no in-state coal supplies. Although the tonnage of imported coal declined by 35 percent between 2008 and 2012, total coal expenditures only dropped 19 percent. This discrepancy is due to the fact that the average price paid for coal in Florida increased from $70.04 per ton to $88.16 per ton, which are among some of the highest prices in the U.S.
Power producers in Florida paid nearly $1.3 billion to import 14.5 million tons of coal from as far away as Colombia. Florida ranks fifth nationally for money spent on net coal imports and second for expenditures on international imports. Seminole Electric Cooperative sent $282 million out of Florida to purchase coal in 2012, more than any other electricity provider in the state. Four additional Florida utilities—JEA, TECO Energy, Gulf Power and Duke Energy—spent more than $100 million on out-of-state imports in 2012.
Florida’s major utilities are required to implement cost effective efficiency programs, but the annual goals last set in 2009 are not being fully achieved. The Florida Public Service Commission (PSC) is currently working with utilities to set new efficiency goals for 2015 and beyond. It is important that the PSC establish meaningful efficiency goals and ensure utilities develop and carry out strong plans for achieving them. Florida is beginning to develop its solar resources, with more 200 MW already installed, including Florida Power and Light’s 25-MW solar photovoltaic facility in DeSoto County. Still, Florida lags behind other leading solar states and lacks sufficient state-wide policies to catch up.
North Carolina
North Carolina is another state without its own coal supplies, and yet it relied on coal for around 41 percent of its in-state electricity generation in 2013. Power producers paid nearly $1.8 billion to import 18.7 million tons of coal to burn in their coal plants, which ranks North Carolina second in the nation for net coal import expenditures. Duke Energy, North Carolina’s largest utility, sent $1.7 billion out of state to purchase coal in 2012 and ranks second among all U.S. power providers for coal import dependency, spending more than $2.2 billion on out-of-state coal across its holdings in six states. Although the total tonnage of imported coal declined by 36 percent since 2008, total coal expenditures dropped only 25 percent as the average price for coal in North Carolina increased from $79.85 per ton to $93.74 per ton.
In 2011, Duke Energy agreed to adopt an annual efficiency savings target of 1 percent starting in 2015. Savings from energy efficiency measures can also count toward a portion of the state’s renewable energy and efficiency resource standard. North Carolina has a wealth of renewable energy resources like sustainable bioenergy, solar and wind; yet these resources supplied just 2.1 percent of the state’s power in 2012. However, utilities are making progress toward meeting a requirement to produce 12.5 percent of the state’s power needs from renewable energy by 2021.
Not only does coal cost our Southeastern states a lot of money, burning coal for electricity also threatens our health on a daily basis. Most recently, a toxic chemical used to process coal leaked from a tank at a Charleston, WV coal plant into the Elk River—resulting in a water ban in nine counties that affects 300,000 residents and causing the governor to declare a state of emergency. This is just the most recent example of one of the myriad dangers communities are exposed to by reliance on coal-fired power. It is time for Southeastern utilities to divest from coal and invest in a clean energy economy.
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Trending
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> [2003]. 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>Wisconsin will end its controversial wolf hunt early after hunters and trappers killed almost 70 percent of the state's quota in the hunt's first 48 hours.
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