Climate change is projected to have adverse impacts on public health. Cobenefits may be possible from more upstream mitigation of greenhouse gases causing climate change. To help measure such cobenefits alongside averted disease-specific risks, a health impact assessment (HIA) framework can more comprehensively serve as a decision support tool. HIA also considers health equity, clearly part of the climate change problem. New choices for energy must be made carefully considering such effects as additional pressure on the world's forests through large-scale expansion of soybean and oil palm plantations, leading to forest clearing, biodiversity loss and disease emergence, expulsion of subsistence farmers, and potential increases in food prices and emissions of carbon dioxide to the atmosphere. Investigators must consider the full range of policy options, supported by more comprehensive, flexible, and transparent assessment methods.

Reducing carbon emissions from deforestation and degradation in developing countries is of central importance in efforts to combat climate change. Key scientific challenges must be addressed to prevent any policy roadblocks. Foremost among the challenges is quantifying nations' carbon emissions from deforestation and forest degradation, which requires information on forest clearing and carbon storage. Here we review a range of methods available to estimate national-level forest carbon stocks in developing countries. While there are no practical methods to directly measure all forest carbon stocks across a country, both ground-based and remote-sensing measurements of forest attributes can be converted into estimates of national carbon stocks using allometric relationships. Here we synthesize, map and update prominent forest biomass carbon databases to create the first complete set of national-level forest carbon stock estimates. These forest carbon estimates expand on the default values recommended by the Intergovernmental Panel on Climate Change's National Greenhouse Gas Inventory Guidelines and provide a range of globally consistent estimates.

Carbon emissions from tropical deforestation have long been recognized as a key component of the global carbon budget, and more recently of our global climate system. Tropical forest clearing accounts for roughly 20% of anthropogenic carbon emissions and destroys globally significant carbon sinks (IPCC 2007). Global climate policy initiatives are now being proposed to address these emissions and to more actively include developing countries in greenhouse gas mitigation (e.g. Santilli et al 2005, Gullison et al 2007). In 2005, at the Conference of the Parties (COP) in Montreal, the United Nations Framework Convention on Climate Change (UNFCCC) launched a new initiative to assess the scientific and technical methods and issues for developing policy approaches and incentives to reduce emissions from deforestation and degradation (REDD) in developing countries (Gullison et al 2007).

Over the last two years the methods and tools needed to estimate reductions in greenhouse gas emissions from deforestation have quickly evolved, as the scientific community responded to the UNFCCC policy needs. This focus issue highlights those advancements, covering some of the most important technical issues for measuring and monitoring emissions from deforestation and forest degradation and emphasizing immediately available methods and data, as well as future challenges.

Elements for effective long-term implementation of a REDD mechanism related to both environmental and political concerns are discussed in Mollicone et al. Herold and Johns synthesize viewpoints of national parties to the UNFCCC on REDD and expand upon key issues for linking policy requirements and forest monitoring capabilities. In response to these expressed policy needs, they discuss a remote-sensing-based observation framework to start REDD implementation activities and build historical deforestation databases on the national level. Achard et al offer an assessment of remote sensing measurements across the world's tropical forests that can provide key consistency and prioritization for national-level efforts. Gibbs et al calculate a range of national-level forest carbon stock estimates that can be used immediately, and also review ground-based and remote sensing approaches to estimate national-level tropical carbon stocks with increased accuracy.

These papers help illustrate that methodologies and tools are indeed available to estimate emissions from deforestation. Clearly, important technical challenges remain (e.g. quantifying degradation, assessing uncertainty, verification procedures, capacity building, and Landsat data continuity) but we now have a sufficient technical base to support REDD early actions and readiness mechanisms for building national monitoring systems.

Thus, we enter the COP 13 in Bali, Indonesia with great hope for a more inclusive climate policy encompassing all countries and emissions sources from both land-use and energy sectors. Our understanding of tropical deforestation and carbon emissions is improving and with that, opportunities to conserve tropical forests and the host of ecosystem services they provide while also increasing revenue streams in developing countries through economic incentives to avoid deforestation and degradation.

This is the story of a powerful historical pathway of structural transformation that is experienced by all successful developing countries; of highly important and diverse approaches to coping with the political pressures generated along that pathway; and of policy mechanisms available to keep the poor from falling off the pathway altogether.  This structural transformation involves four main features: a falling share of agriculture in economic output and employment, a rising share of urban economic activity in industry and modern services, migration of rural workers to urban settings, and a demographic transition in birth and death rates that always leads to a spurt in population growth before a new equilibrium is reached.

At one level, the story is easy to tell because the statistical picture presented, both graphically and econometrically, is, well, telling.  In their broad sweep and relevance, these are very robust results that have very deep historical roots.  Challenging them is like challenging the tides.

At another level, the complexity of national diversity asserts itself in very important ways.  This finding does not alter the pathways themselves, but rather their consequences for income distribution and the gap in labor productivity between urban and rural economies.  We learn a lot about the possibilities for narrowing this gap during the process of structural transformation by comparing the historical experience of rapidly growing Asia with the rest of the world.  Individual country experience is revealing as well.  The stress placed on this productivity gap, how it changes during the structural transformation, and potential policy interventions to narrow it, is the major contribution of this monograph.

Making sure the poor are connected to both the structural transformation and to the policy initiatives designed to ameliorate the distributional consequences of rapid transformation has turned out to be a major challenge for policy makers over the past half century.  There are successes and failures, and the historical record illuminates what works and what does not.  Trying to stop the structural transformation does not work, at least for the poor.  Investing in the capacity of the poor to cope with change and to participate in its benefits through better education and health does seem to work.  Such investments typically require significant public sector resources and policy support, and thus depend on political processes that are themselves conditioned by the pressures generated by the structural transformation.

Trends in recent temperature observations and model projections of the future are characterized by greater warming of daily minimum (tmin) relative to maximum (tmax) temperatures. To aid understanding of how tmin and tmax differentially affect crop yields, we analyzed variations of regional spring wheat yields and temperatures for three irrigated sites in western North America that were characterized by low correlations between tmin and tmax. The crop model CERES-Wheat v3.5 was evaluated in each site and used to project future response to temperature changes. Tmin and tmax exhibited distinct historical correlations with yields, with CERES successfully capturing the observed relationships in each region. In the Yaqui Valley of Mexico, historical yields were strongly correlated with tmin but not tmax. However, CERES projections of response to increased tmin or tmax (holding other variables constant) were similar (6% °C^-1), indicating that the apparent historical importance of tmin mainly results from covariation between temperatures and solar radiation and not greater direct effects of tmin on yields. In the San Luis-Mexicali Valley of Mexico and in the Imperial Valley of California, the opposite was observed: historical yield correlations with tmin and tmax were similar, but projected responses to tmax were roughly three times larger than tmin. The latter is explained by opposing effects of tmin and tmax on grain filling rates in CERES, with higher tmin increasing harvest indices. This model mechanism was not clearly supported by historical data and remains an area of uncertainty for projecting yield responses to climate change.

Several impacts of climate change may depend more on changes in mean daily minimum (Tmin) or maximum (Tmax) temperatures than daily averages. To evaluate uncertainties in these variables, we compared projections of Tmin and Tmax changes by 2046-2065 for 12 climate models under an A2 emission scenario. Average modeled changes in Tmin were similar to those for Tmax, with slightly greater increases in Tmin consistent with historical trends exhibiting a reduction in diurnal temperature ranges. In contrast, the inter-model variability of Tmin and Tmax projections exhibited substantial differences. For example, inter-model standard deviations of June-August Tmax changes were more than 50% greater than for Tmin throughout much of North America, Europe, and Asia. Model differences in cloud changes, which exert relatively greater influence on Tmax during summer and Tmin during winter, were identified as the main source of uncertainty disparities. These results highlight the importance of considering separately projections for Tmax and Tmin when assessing climate change impacts, even in cases where average projected changes are similar. In addition, impacts that are most sensitive to summertime Tmin or wintertime Tmax may be more predictable than suggested by analyses using only projections of daily average temperatures.

This paper provides an original account of global land, water and nitrogen use in support of industrialized livestock production and trade, with emphasis on two of the fastest growing sectors, pork and poultry. Our analysis focuses on trade in feed and animal products, using a new model that calculates the amount of "virtual" nitrogen, water and land used in production but not embedded in the product. We show how key meat importing countries, such as Japan, benefit from "virtual" trade in land, water and nitrogen, and how key meat exporting countries, such as Brazil, provide these resources without accounting for their true environmental cost. Results show that Japan's pig and chicken meat imports embody the virtual equivalent of 50% of Japan's total arable land, and half of Japan's virtual nitrogen total is lost in the US. Trade links with China are responsible for 15% of the virtual nitrogen left behind in Brazil due to feed and meat exports, and 20% of Brazil's area is used to grow soybean exports. The complexity of trade in meat, feed, water and nitrogen, is illustrated by the dual roles of the US and the Netherlands as both importers and exporters of meat. Mitigating environmental damage from industrialized livestock production and trade depends on a combination of direct pricing strategies, regulatory approaches and use of best management practices. Our analysis indicates that increased water and nitrogen use efficiency and land conservation resulting from these measures could significantly reduce resource costs.