“Of the 1 billion people who are in food-stressed situations today, a significant proportion live in conflict-ridden countries,” said Raymond Gilpin of the U.S. Institute of Peace at last Thursday’s launch of USAID’s Feed the Future initiative. “Most of them live in fear for their lives, in uncertain environments, and without clear hope for a better tomorrow.”

According to data from the World Food Programme and the UCDP/PRIO Armed Conflict Database, of the countries with moderately to very high hunger rates in 2009, nearly a quarter experienced violent conflict in the previous year, and nearly half in the preceding two decades.

The Economist published this week For Want of a Drink, its special report on water. The report, a compilation of 11 articles, is a mix of surveys of global management strategies, health impacts, and economic considerations on the one hand, and deeper looks into specific practices in Singapore, India, and China on the other. Most interesting for New Security Beat readers is the article “To the Last Drop: How to Avoid Water Wars.” The article states there have been “no true water wars,” but goes on to question whether the pressures of climate change and population growth could generate different outcomes in the future. While the challenges laid out are manifold, the article concludes that potential for cooperation is equally present. “The secret is to look for benefits and then try to share them. If that is done, water can bring competitors together.”

Part of USAID’s FRAMEweb community, GeoExplorer is a visual aggregator of natural resource management (NRM) activities, best practices, success stories, and lessons learned. As of launch, GeoExplorer is home to 43 activities, each searchable by scale (e.g. local, national, or regional), sub-sector (e.g. forestry, water, wildlife), and topic (e.g. governance, livelihoods, and health).

GeoExplorer was designed to foster knowledge sharing among and between practitioners, program managers, and researchers. USAID expects the tool to help avoid cases both of repeating past mistakes and reinventing the wheel, serve as a guide for study trips to the field, build community exchanges, and foster networking. It is built on ArcGIS architecture that USAID hopes will allow for continual expansion, particularly through the addition of GIS layers that can empower users with greater search options and tools for cross-project analytic analyses.

“Population pressures and diminishing land holdings – due to high fertility rates, war and genocide, and subsequent migration – have caused a rapid decrease in the forested and protected areas and increased soil infertility and food insecurity” in Rwanda, USAID’s Irene Kitzantides told a Wilson Center audience.

Kitzantides, a population, health, and environment advisor and global health fellow, said “the population is projected to reach over 14 million by 2025” – nearly one-third more than today, due to the country’s high fertility rate of nearly 5.5 children per women–which could continue to negatively impact forests and food supplies.

“Population, Health and Human Settlements” is the second chapter in the Rwandan government’s Rwanda State of Environment and Outlook. The chapter highlights the Rwandan government’s recognition of the interconnections between population, health, and environment, noting that population “can influence the state of the environment” and pose strains “on available public infrastructure, limited land, and natural resources.” The chapter examines Rwanda’s population growth and distribution, the state of “environmental health” in rural and urban areas, and health indicators relating to child and maternal health and HIV/AIDS. It goes on to describe government strategies to “improve settlements and human welfare.” “As population pressure is one of the key drivers of environmental degradation and poverty,” the chapter’s authors write, “the implementation of the population policy, especially aspects that address high fertility rates, gender and reproductive health, migration and human settlements” is increasingly important.

“Policies that have the effect…of leading to lower fertility and to slower population growth can be considered ‘win-win’ from the climate point of view,” said Brian O’Neill of the National Center for Atmospheric Research (NCAR) at a recent Wilson Center event on his latest research. Yet while the connection between demographic change and CO2 emissions is implicitly understood among both researchers and policymakers, it “has not really caught on,” he added.

In response to critiques of earlier studies looking at demographic change and CO2 emissions—which the climate research community has faulted for their lack of sophistication, use of unfamiliar analytic approaches, and failure to clearly demonstrate the magnitude of the connection—O’Neill is using a novel, more rigorous approach that he hopes will provide a clearer understanding of the links between demography and climate change.

The Population Factor

To generate CO2 emissions scenarios for the next 100 years, O’Neill’s team used the Population-Environment-Technology (PET) Model, originally created by Lawrence H. Goulder and Michael Dalton of Stanford. The PET Model takes its basic assumptions on regional economic growth, technological development, change in population characteristics, and other factors from the IPCC’s A2 and B2 scenarios, but replaces each scenario’s singular population growth curve with high and low alternatives from the United Nations Population Division.

Like other climate models, the PET Model divides the world into nine regions. “You don’t want to treat economies—or the demography, the consumption patterns, the energy system, and so on—of sub-Saharan Africa the same as you do for the U.S. or EU,” he said.

What makes the O’Neill’s approach unique, however, is his attention to the sub-regional level. “Typically in these models…you break the world up into nine regions, but then you treat everyone in sub-Saharan Africa the same, everyone in China the same,” said O’Neill.

By drawing on data from detailed surveys of 800,000 households from 35 countries, O’Neill and his team demonstrated that the distinctions between urban and rural, older and younger, and smaller and larger households hold important implications for carbon emissions. This inclusion of demographic sub-factors allows a deeper degree of analysis than models that treat all households the same. They found that age structure, household size, and urbanization all altered emissions outputs.

Could Bending Population Growth Curves Reduce Emissions?

In the long run, the potential for demographic shifts to reduce CO2 emissions “is a big number,” said O’Neill. In the medium term, for example by the middle of the century, results are less clear. To compare population-related emissions reductions to other carbon-reduction opportunities, he evoked Socolow and Pacala’s “stabilization wedge” framework. The wedge framework posits 15 opportunities, or wedges, to eventually reduce CO2 emissions by 1 billion tons of carbon equivalent per year. They contend that implementing any 7 of the wedges could stabilize CO2 emissions by 2050 and, if followed by additional measures reducing emissions below today’s levels, would stabilize the atmospheric concentration of CO2 at 550 ppm and forestall the worst impacts of climate change.

Would reducing population growth equal a wedge? The full results, currently under review at a scientific journal, will seek to answer this question. “Slower population growth can’t solve the climate problem,” he concluded. “But it can certainly help.”

C-Learn, developed by the coalition group Climate Interactive, is a three-region climate simulator that allows users to input targets for fossil fuels emissions, emissions from deforestation, and reductions from afforestation. The app then outputs the expected results for atmospheric CO2 concentrations and global temperature. The simulator is a free, public version of the more complicated C-ROADS, a tool designed to help policymakers compare the predicted effects of particular climate change mitigation policies.

Although interesting, C-Learn notably lacks the option to input assumptions regarding population growth rates, let alone those concerning more nuanced factors such as age structure, urbanization rates, or household size, the importance of which were recently discussed at the Wilson Center by the National Center for Atmospheric Research‘s Brian O’Neill. Population is a significant factor in determining what can be expected as reasonable emission targets and the failure to include space for demographic assumptions is a significant short-coming.

“The MAHB, the Culture Gap, and Some Really Inconvenient Truths,” authored by Paul Ehrlich and appearing in the most recent edition of PLoS Biology, is a call for greater participation in the Millennium Assessment of Human Behavior (MAHB). MAHB was created, he writes, because societies understand the magnitude of environmental challenges, yet often still fail to act. “The urgent need now is clearly not for more natural science…but rather for better understanding of human behaviors and how they can be altered to direct Homo sapiens onto a course toward a sustainable society.” MAHB aims to create an inclusive global discussion of “the human predicament, what people desire, and what goals are possible to achieve in a sustainable society” in the hopes of encouraging a “rapid modification” in human behavior.