Low-Carbon Economies Most Promising Pathway to Meaningful Global Climate Agreement
As the world gathers in Lima to discuss next year’s climate deadline, a lot of focus is on the U.S.-China climate agreement. While alone that deal has not paved a pathway for a meaningful global agreement all the way to Paris, if you detour through New Delhi something intriguing and hopeful emerges.
I was sitting in a New Delhi conference room filled with U.S. and Indian climate experts and diplomats preparing to discuss our bilateral issues when the U.S.-China announcement was made. The shock was palpable—the Indians felt that China had betrayed the block of emerging nations—but out of their conversations emerged, in my mind, the most hopeful road yet to a meaningful, if still only partial, global climate accord.
Many of the Indians had expected China to abandon their common front—but China’s agreement to a date—2030—to cap its emissions was the big jolt. The Indians, correctly, felt that by making an agreement with the U.S. China had set up India as a major global target, and that they would now be under intense pressure to set a date for their emissions to peak—which they will not do. That pressure has now begun. India is the new climate villain.
Indians don’t understand why. The U.S. emits 17 tons of CO2 per person; China 8 tons; India only 1.7. The U.S. emits only 81 pounds of CO2 for every dollar of economic output; India 260 pounds; China a whopping 380 pounds.
India is not willing to set a date on which its emissions will peak—because most of its industrial base, transportation networks and urban infrastructure have not yet been built. But it is willing to negotiate around the carbon intensity of its economy—and potentially to be quite ambitious. (It had already agreed to cut carbon intensity 25 percent below 2005 levels by 2020.) The new government has increased the clean energy target five-fold, to 10 gigawatts per year, and wants to phase out coal imports within three years.
Carbon intensity is the pathway that really matters in the next round of global climate negotiations. Nations don’t size their economic aspirations to their carbon goals. They project how big an economy they anticipate—or desire—and set a more or less ambitious goal for reducing its carbon intensity with efficiency, renewables or other programs. Climate scientists then translate these commitments into total greenhouse pollution, and tell us how far we are from where we need to be—multiplying promised intensity by projected economic size. But what nations actually alter through climate policy is the carbon intensity, not the size, of their future economy.
India, then, wants to negotiate around the basic policy variable countries wrestle with—how rapidly they will replace dirty fossil energy with more productive, efficient and clean energy. This variable is also one which accommodates the broad diversity of emerging market economies, from a grossly inefficient massive emitter like China through a mid-range but still fairly inefficient economy like India’s to smaller, rural African countries with tiny energy consumption, most of it biomass. Technological progress is easily modeled in an energy intensity context—cheaper solar allows every nation with sunshine to be more ambitious about its carbon intensity—even if as a result it grows, and sheds poverty faster. (The Government of India has estimated that in a business as usual strategy the carbon intensity of its economy would come down by 22 percent by 2030, but with low carbon policies it could come down 42 percent. In its April 2014 report on low-carbon growth, the Indian Planning Commission estimated that India could bring its 2030 emissions down from 3.6 tons per capita to only 2.6 tons, but that the additional investment required would be 1.5 percent of GDP.
The Indians argue that if low carbon energy costs poor countries more than their available fossil choices, the Global North, which created the climate crisis, should help them finance the transition. The rich nations have thus far been unwilling to offer what the Global South views as adequate funding.
Clearly, however, the cheaper clean energy gets, the smaller such a premium becomes. So the conversation then shifts to “how do we make clean energy cheaper for poor countries?” That's a question to which there are many win-win solutions since most of them make energy cheaper for the Organization for Economic Cooperation and Development (OECD) nations as well. (There is still the problem of paying for the damages caused by climate disruption already occurring—really, shouldn’t the polluter pay?)
Negotiating only on carbon intensity does leave the possibility that as nations advance economically, they never limit and reduce total emissions. The previous Indian government promised that India would accept a per capita carbon goal—the average level of emissions from OECD nations. The average Indian would always emit less carbon than the average citizen of industrial nations. As OECD per capita emission targets came down, so would India’s per capita goal. This kind of “cap” appeared to be still on the table—if the wealthy countries make good faith efforts to help emerging economies with finance and technology transfer.
The problem is thus not that carbon intensity is the wrong measure—it’s that our clean energy goals are not ambitious enough.
Events since our Delhi meeting and the U.S.-China deal strongly suggest that a climate pathway can be threaded through India. Late in November, India and China agreed with longstanding U.S. suggestions that hydrofluorocarbons (HFCs)—the most potent known greenhouse pollutant—should be regulated under the existing Montreal Ozone Protocol, which already provides financing mechanisms and is able to move quickly. President Obama has indicated that he will go to India in the new year, setting the stage for more conversations. India has demonstrated its ability to bring renewable energy into the marketplace at a lower cost than its Asian neighbors, with recent solar bids being lower than electricity from imported coal.
But the historic focus of climate diplomacy on caps and cuts may slow down progress on the pathway which really offers the greatest initial promise—a recognition that rapidly lowering the carbon intensity of every nation’s economy, while making low carbon economies more dynamic and robust, is the most promising pathway to a critical mass of ambitious climate commitments in Paris.
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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.