As India's middle class has expanded, the nation's public health concerns have shifted. Obesity rates have risen, coinciding with a surge in diabetes. The number of Indians with type 2 diabetes is expected to double by 2030 to nearly 80 million. Meanwhile, 43 percent of children in India are underweight.
Expanding croplands to meet the needs of a growing population, changing diets, and biofuel production comes at the cost of reduced carbon stocks in natural vegetation and soils. Here, we present a spatially explicit global analysis of tradeoffs between carbon stocks and current crop yields. The difference among regions is striking. For example, for each unit of land cleared, the tropics lose nearly two times as much carbon (∼120 tons·ha-1 vs. ∼63 tons·ha-1) and produce less than one-half the annual crop yield compared with temperate regions (1.71 tons·ha-1·y-1 vs. 3.84 tons·ha-1·y-1). Therefore, newly cleared land in the tropics releases nearly 3 tons of carbon for every 1 ton of annual crop yield compared with a similar area cleared in the temperate zone. By factoring crop yield into the analysis, we specify the tradeoff between carbon stocks and crops for all areas where crops are currently grown and thereby, substantially enhance the spatial resolution relative to previous regional estimates. Particularly in the tropics, emphasis should be placed on increasing yields on existing croplands rather than clearing new lands. Our high-resolution approach can be used to determine the net effect of local land use decisions.
Predicting the potential effects of climate change on crop yields requires a model of how crops respond to weather. As predictions from different models often disagree, understanding the sources of this divergence is central to building a more robust picture of climate change's likely impacts. A common approach is to use statistical models trained on historical yields and some simplified measurements of weather, such as growing season average temperature and precipitation. Although the general strengths and weaknesses of statistical models are widely understood, there has been little systematic evaluation of their performance relative to other methods. Here we use a perfect model approach to examine the ability of statistical models to predict yield responses to changes in mean temperature and precipitation, as simulated by a process-based crop model. The CERES-Maize model was first used to simulate historical maize yield variability at nearly 200 sites in Sub-Saharan Africa, as well as the impacts of hypothetical future scenarios of 2◦C warming and 20% precipitation reduction. Statistical models of three types (time series, panel, and cross-sectional models) were then trained on the simulated historical variability and used to predict the responses to the future climate changes. The agreement between the process-based and statistical models' predictions was then assessed as a measure of how well statistical models can capture crop responses to warming or precipitation changes. The performance of statistical models differed by climate variable and spatial scale, with time-series statistical models ably reproducing site-specific yield response to precipitation change, but performing less well for temperature responses. In contrast, statistical models that relied on information from multiple sites, namely panel and cross-sectional models, were better at predicting responses to temperature change than precipitation change. The models based on multiple sites were also much less sensitive to the length of historical period used for training. For all three statistical approaches, the performance improved when individual sites were first aggregated to country-level averages. Results suggest that statistical models, as compared to CERES-Maize, represent a useful if imperfect tool for projecting future yield responses, with their usefulness higher at broader spatial scales. It is also at these broader scales that climate projections are most available and reliable, and therefore statistical models are likely to continue to play an important role in anticipating future impacts of climate change.
Join us for a discussion on human rights and environmental justice implications surrounding the BP oil spill from regulatory, litigation and conceptual perspectives.
Introduced and moderated by Dr. Helen Stacy, Co-ordinator, Program on Human Rights in the Center on Democracy, Development and Rule of Law and Senior Fellow, Freeman Spogli Institute.
Panelists:
Meg Caldwell, Director, Environmental and Natural Resources Law & Policy Program; Executive Director, Center for Ocean Solutions, Woods Institute for the Environment. Professor Caldwell's scholarship focuses on the environmental effects of local land use decisions, the use of science in environmental and marine resource policy development and implementation, and developing private and public incentives for natural resource conservation.
Deborah Sivas, Luke W. Cole Professor of Environmental Law and Director, Environmental Law Clinic. Professor Sivas's current research is focused on the interaction of law and science in the arena of climate change and coastal/marine policy and the ability of the public to hold policymakers accountable.
Ursula Heise, Director, Program in Modern Thought & Literature and Professor of English; member of the Executive Committee of the Program in Science, Technology & Society; Affiliated Faculty of the Woods Institute for the Environment. Author of Sense of Place and Sense of Planet: The Environmental Imagination of the Global (Oxford University Press, 2008), After Nature: Species Extinction and Modern Culture, (forthcoming) and The Avantgarde and the Forms of Nature (in progress).
Stanford Law School
Room 280B
FSI's 2010 Fall Orientation welcomed faculty, staff, researchers, and friends of the institute to the new academic year and highlighted the institute's diverse research collaborations, educational programs, and policy engagement. Presentations on display and in live video offered highlights of the current work of FSI centers and programs on many of the most challenging issues of the day. In his welcoming remarks, FSI Director Coit Blacker emphasized the interdisciplinary, cross-campus nature of FSI's work and thanked the FSI community for their many contributions to new knowledge and new approaches to many of the most pressing issues on today's global agenda.
This year's Orientation attracted the largest turnout to date. On continual display was a slide show capturing research of FSI centers and programs in the field and multi-disciplinary work here at the institute, along with highlights of FSI conferences, lectures, and policy endeavors compiled by FSI's Nora Sweeny.
Among the highlights were the following displays:
Other highlights included the presentations prepared by Stanford students who worked in the field this past summer. One group worked in China, developing a survey on nutrition and anemia and their effect on learning, with FSI's Scott Rozelle, Director of the Rural Education Action Program. A second group helped Dr. Paul Wise, professor of pediatrics and Stanford Health Policy core faculty member, evaluate prenatal care in the rural highlands of Guatemala.
Sectoral crediting mechanisms such as sectoral no-lose targets have been proposed as a way to provide incentives for emission reductions in developing countries as part of an international climate agreement, and scale up carbon trading from the project-level Clean Development Mechanism to the sectoral level.
Countries would generate tradable emission credits (offsets) for reducing emissions in a sector below an agreed crediting baseline. However, large uncertainties in the regulator's predictions of the counterfactual business-as-usual baseline are likely to render sectoral no-lose targets an extremely unattractive mechanism in practice, at least for the transportation case study presented here. Given these uncertainties, the regulator faces a tradeoff between efficiency (setting generous crediting baselines to encourage more countries to opt in) and limiting transfer payments for non-additional offsets (which are generated if the crediting baseline is set above business-as-usual).
The first-best outcome is attainable through setting a generous crediting baseline. However, this comes at the cost of either increased environmental damage (if developed country targets are not adjusted to account for non-additional offsets), or transfers from developed to developing countries that are likely to be too high to be politically feasible (if developed country targets are made more stringent in recognition that many offsets are nonadditional). A more stringent crediting baseline still generates a large proportion of non-additional offsets, but renders sectoral no-lose targets virtually irrelevant as few countries opt in.
