Operationalizing the social-ecological systems framework to assess sustainability.

Environmental governance is more effective when the scales of ecological processes are well matched with the human institutions charged with managing human-environment interactions. The social-ecological systems (SESs) framework provides guidance on how to assess the social and ecological dimensions that contribute to sustainable resource use and management, but rarely if ever has been operationalized for multiple localities in a spatially explicit, quantitative manner. Here, we use the case of small-scale fisheries in Baja California Sur, Mexico, to identify distinct SES regions and test key aspects of coupled SESs theory. Regions that exhibit greater potential for social-ecological sustainability in one dimension do not necessarily exhibit it in others, highlighting the importance of integrative, coupled system analyses when implementing spatial planning and other ecosystem-based strategies.

A unified framework for measuring preferences for schools and neighborhoods

This paper develops a framework for estimating household preferences for school and neighborhood attributes in the presence of sorting. It embeds a boundary discontinuity design in a heterogeneous residential choice model, addressing the endogeneity of school and neighborhood characteristics. The model is estimated using restricted-access Census data from a large metropolitan area, yielding a number of new results. First, households are willing to pay less than 1 percent more in house prices - substantially lower than previous estimates - when the average performance of the local school increases by 5 percent. Second, much of the apparent willingness to pay for more educated and wealthier neighbors is explained by the correlation of these sociodemographic measures with unobserved neighborhood quality. Third, neighborhood race is not capitalized directly into housing prices; instead, the negative correlation of neighborhood percent black and housing prices is due entirely to the fact that blacks live in unobservably lower-quality neighborhoods. Finally, there is considerable heterogeneity in preferences for schools and neighbors, with households preferring to self-segregate on the basis of both race and education. © 2007 by The University of Chicago. All rights reserved.

A COMPARISON OF MARKET CLEARING TOOLS IN ELECTRICITY SYSTEMS

To maintain a strict balance between demand and supply in the US power systems, the Independent System Operators (ISOs) schedule power plants and determine electricity prices using a market clearing model. This model determines for each time period and power plant, the times of startup, shutdown, the amount of power production, and the provisioning of spinning and non-spinning power generation reserves, etc. Such a deterministic optimization model takes as input the characteristics of all the generating units such as their power generation installed capacity, ramp rates, minimum up and down time requirements, and marginal costs for production, as well as the forecast of intermittent energy such as wind and solar, along with the minimum reserve requirement of the whole system. This reserve requirement is determined based on the likelihood of outages on the supply side and on the levels of error forecasts in demand and intermittent generation. With increased installed capacity of intermittent renewable energy, determining the appropriate level of reserve requirements has become harder. Stochastic market clearing models have been proposed as an alternative to deterministic market clearing models. Rather than using a fixed reserve targets as an input, stochastic market clearing models take different scenarios of wind power into consideration and determine reserves schedule as output. Using a scaled version of the power generation system of PJM, a regional transmission organization (RTO) that coordinates the movement of wholesale electricity in all or parts of 13 states and the District of Columbia, and wind scenarios generated from BPA (Bonneville Power Administration) data, this paper explores a comparison of the performance between a stochastic and deterministic model in market clearing. The two models are compared in their ability to contribute to the affordability, reliability and sustainability of the electricity system, measured in terms of total operational costs, load shedding and air emissions. The process of building the models and running for tests indicate that a fair comparison is difficult to obtain due to the multi-dimensional performance metrics considered here, and the difficulty in setting up the parameters of the models in a way that does not advantage or disadvantage one modeling framework. Along these lines, this study explores the effect that model assumptions such as reserve requirements, value of lost load (VOLL) and wind spillage costs have on the comparison of the performance of stochastic vs deterministic market clearing models.

A framework for revising preservice curriculum for nonphysician clinicians: The mozambique experience.

Mozambique, with approximately 0.4 physicians and 4.1 nurses per 10,000 people, has one of the lowest ratios of health care providers to population in the world. To rapidly scale up health care coverage, the Mozambique Ministry of Health has pushed for greater investment in training nonphysician clinicians, Tιcnicos de Medicina (TM). Based on identified gaps in TM clinical performance, the Ministry of Health requested technical assistance from the International Training and Education Center for Health (I-TECH) to revise the two-and-a-half-year preservice curriculum. A six-step process was used to revise the curriculum: (i) Conducting a task analysis, (ii) defining a new curriculum approach and selecting an integrated model of subject and competency-based education, (iii) revising and restructuring the 30-month course schedule to emphasize clinical skills, (iv) developing a detailed syllabus for each course, (v) developing content for each lesson, and (vi) evaluating implementation and integrating feedback for ongoing improvement. In May 2010, the Mozambique Minister of Health approved the revised curriculum, which is currently being implemented in 10 training institutions around the country. Key lessons learned: (i) Detailed assessment of training institutions' strengths and weaknesses should inform curriculum revision. (ii) Establishing a Technical Working Group with respected and motivated clinicians is key to promoting local buy-in and ownership. (iii) Providing ready-to-use didactic material helps to address some challenges commonly found in resource-limited settings. (iv) Comprehensive curriculum revision is an important first step toward improving the quality of training provided to health care providers in developing countries. Other aspects of implementation at training institutions and health care facilities must also be addressed to ensure that providers are adequately trained and equipped to provide quality health care services. This approach to curriculum revision and implementation teaches several key lessons, which may be applicable to preservice training programs in other less developed countries.