No abstract

No abstract

A large fraction of anthropogenic CO 2 emissions comes from large point sources such as power plants, petroleum refineries, and large industrial facilities. The existence and locations of these facilities depend on a variety of factors that include the distribution of natural resources and the economy of scale of operating large facilities. These large facilities provide goods and/or services well beyond the political jurisdiction in which they reside and their emissions to the global atmosphere are not a simple reflection of the consumption of goods and services within the geographic region in which they reside. And yet many accounting schemes do not distinguish between emissions for local consumption and emissions for export. Looking at the geographic distribution of large point sources of CO 2 emissions in the U.S. suggests that per capita emissions from a geographic area are not necessarily a good indication of the mitigation responsibility of the residents. The design of effective and fair mitigation strategies needs to consider that emissions embodied in the products of large facilities, such as electric power and refined petroleum products, are often transferred across accounting boundaries; e.g. the CO 2 emissions occur in one jurisdiction even though the electricity is used in another. We close with a short discussion of how two sub-national emissions trading schemes in the U.S. have confronted the issue of embodied emissions crossing their jurisdictional boundaries.

No abstract

Traditional smallholder farming systems dominate the savanna range countries of sub-Saharan Africa and provide the foundation for the region's food security. Despite continued expansion of smallholder farming into the surrounding savanna landscapes, food insecurity in the region persists. Central to the monitoring of food security in these countries, and to understanding the processes behind it, are reliable, high-quality datasets of cultivated land. Remote sensing has been frequently used for this purpose but distinguishing crops under certain stages of growth from savanna woodlands has remained a major challenge. Yet, crop production in dryland ecosystems is most vulnerable to seasonal climate variability, amplifying the need for high quality products showing the distribution and extent of cropland. The key objective in this analysis is the development of a classification protocol for African savanna landscapes, emphasizing the delineation of cropland. We integrate remote sensing techniques with probabilistic modeling into an innovative workflow. We OPEN ACCESSRemote Sens. 2015, 7 15296 present summary results for this methodology applied to a land cover classification of Zambia's Southern Province. Five primary land cover categories are classified for the study area, producing an overall map accuracy of 88.18%. Omission error within the cropland class is 12.11% and commission error 9.76%.