Food as the basis for development and security: A strategy for Yemen

Yemen has been facing severe development challenges in recent years, but rapidly depleting oil and water resources combined with large population increases and a lack of job-creating growth are making a difficult situation even more complicated. In order to provide opportunities for Yemenis to escape the current situation of widespread poverty and food insecurity, the Government of the Republic of Yemen, under the leadership of the Ministry of Planning and International Cooperation, has developed a strategy to help all Yemeni people gain access to sufficient and nutritious foods in order to live active, productive, and healthy lives. The main objectives of the National Food Security Strategy, developed with the support of the International Food Policy Research Institute, are to (1) cut food insecurity by one-third by 2015, (2) reach moderate food security levels—meaning 90 percent of people have enough to eat year-round—by 2020, and (3) reduce child malnutrition by at least one percentage point per year. As a contribution to this process, the authors of this paper identify seven priority actions to help achieve these goals. 1. Leverage the fuel-subsidy reform process to promote food security. 2. Improve the business climate to foster pro-food-secure private investments in promising sectors. 3. Use qat reduction policies to enhance agricultural development. 4. Strengthen food security risk-management practices. 5. Implement the water-sector strategy decisively. 6. Target public investment to the food insecure more accurately and improve service provision, especially in rural areas. 7. Launch high-level awareness campaigns for family planning, healthy nutrition, and women's empowerment. The government, civil society groups, and international partners need to quickly, decisively, and jointly implement these seven actions in order to fulfill the strategic goals. The implementation process is likely to be most effective if conducted in a transparent and inclusive manner with effective follow-up and appropriate monitoring and evaluation mechanisms.food security, Poverty, Economic development,

Transport mode and network architecture : carbon footprint as a new decision metric

Thesis (M. Eng. in Logistics)--Massachusetts Institute of Technology, Engineering Systems Division, 2008.Includes bibliographical references (leaves 132-133).This thesis examines the tradeoffs between carbon footprint, cost, time and risk across three case studies of United States' perishable or consumer packaged goods firms and their transportation partners. Building upon previous research, and utilizing an Institute of Management and Administration (IOMA) and MIT Center for Transportation and Logistics (CTL) survey of supply chain professionals, the goal of this thesis is to better understand the decision process and motivations of our case study companies with regard to carbon footprint and implications for transport mode and network architecture, and the tradeoffs involved in making these decisions. We examine: (1) An expedited refrigerated rail service providing coast-to-coast shipment of produce for a major retailer, in lieu of its prior trucking arrangement; (2) A dairy producer which with the help of its trucking partner switched from less-than-truckload (LTL) to full truckload (FTL) and currently explore the possibility to re-organize its distribution network; and (3) A bottled water firm which created an additional container shipping route to reduce the volume of water it ships via truck. Comparisons and contrasts are made between case study firms. Findings from these case studies are used to make forward-looking recommendations for companies interested in altering transport mode and/or network architecture as a means of reducing the carbon footprint of their operations.by Nelly Andrieu and Lee Weiss.M.Eng.in Logistic

A Survey of Green Networking Research

Reduction of unnecessary energy consumption is becoming a major concern in wired networking, because of the potential economical benefits and of its expected environmental impact. These issues, usually referred to as "green networking", relate to embedding energy-awareness in the design, in the devices and in the protocols of networks. In this work, we first formulate a more precise definition of the "green" attribute. We furthermore identify a few paradigms that are the key enablers of energy-aware networking research. We then overview the current state of the art and provide a taxonomy of the relevant work, with a special focus on wired networking. At a high level, we identify four branches of green networking research that stem from different observations on the root causes of energy waste, namely (i) Adaptive Link Rate, (ii) Interface proxying, (iii) Energy-aware infrastructures and (iv) Energy-aware applications. In this work, we do not only explore specific proposals pertaining to each of the above branches, but also offer a perspective for research.Comment: Index Terms: Green Networking; Wired Networks; Adaptive Link Rate; Interface Proxying; Energy-aware Infrastructures; Energy-aware Applications. 18 pages, 6 figures, 2 table

Spatial Decision Support Systems for Sustainable Urban Redevelopment

A recent United Nations study concludes that worldwide population will grow from approximately 7 billion today to 9.3 billion in 2050 and 10.1 billion in 2100. Nowhere is this population growth more evident than in the major cities of the world. For the first time in history, a majority of the world’s people lived in cities. In 1950, by comparison, less than 30% of the world’s population dwelled in cities. This rapid growth of population, coupled with an aging infrastructure, and the abandoning of urban manufacturing sites, creates an urgent need for inner city revitalization. There are several urban areas especially at risk. They include cities with high concentrations of derelict properties and vulnerable populations that are located within the urban core. Others include sites that are in proximity to urban industrial riverfronts. These sites are collectively known as Brownfields. Also included are sites, including Public Complexes (e.g. large publicly owned campuses such as colleges, universities, prisons, and hospital centers), with an expansive campus footprint, “where storm water runoff occurs instead of soaking into the ground” (Rutgers, 2014). As global population continues to increase in these areas, researchers are investigating new techniques that promote economic growth and sustainable development, while minimizing the environmental, social, and economic impacts of urban sprawl. One such technique is building green buildings on these Brownfield Sites. The present study investigates whether a prescriptive approach to urban development, the third party rating system, coupled with a Business Intelligence Dashboard, as a data visualization tool to display the status of redevelopment, can provide feasible and intuitive integration of data in which to prioritize redevelopment. The study presents a new framework and key sustainability indicators, based on existing third party rating systems, to prioritize redevelopment. It introduces these assessments into a Spatial Decision Support System, utilizing a dashboard as an interactive tool to gather and consolidate data and to present an evaluative means for decision-makers. The tool allows identification of the highest priority sites for long-term and short-term redevelopment of distressed properties. The aim of the research is to advance knowledge for new concepts for sustainable urban redevelopment projects using decision frameworks for selection among alternative Brownfield redevelopment projects. The study indicates that the third party rating system, coupled with dashboards, is an effective decision support tool that facilitates efficient decision-making

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter

Model-Based Design, Analysis, and Implementations for Power and Energy-Efficient Computing Systems

Modern computing systems are becoming increasingly complex. On one end of the spectrum, personal computers now commonly support multiple processing cores, and, on the other end, Internet services routinely employ thousands of servers in distributed locations to provide the desired service to its users. In such complex systems, concerns about energy usage and power consumption are increasingly important. Moreover, growing awareness of environmental issues has added to the overall complexity by introducing new variables to the problem. In this regard, the ability to abstractly focus on the relevant details allows model-based design to help significantly in the analysis and solution of such problems. In this dissertation, we explore and analyze model-based design for energy and power considerations in computing systems. Although the presented techniques are more generally applicable, we focus their application on large-scale Internet services operating in U.S. electricity markets. Internet services are becoming increasingly popular in the ICT ecosystem of today. The physical infrastructure to support such services is commonly based on a group of cooperative data centers (DCs) operating in tandem. These DCs are geographically distributed to provide security and timing guarantees for their customers. To provide services to millions of customers, DCs employ hundreds of thousands of servers. These servers consume a large amount of energy that is traditionally produced by burning coal and employing other environmentally hazardous methods, such as nuclear and gas power generation plants. This large energy consumption results in significant and fast-growing financial and environmental costs. Consequently, for protection of local and global environments, governing bodies around the globe have begun to introduce legislation to encourage energy consumers, especially corporate entities, to increase the share of renewable energy (green energy) in their total energy consumption. However, in U.S. electricity markets, green energy is usually more expensive than energy generated from traditional sources like coal or petroleum. We model the overall problem in three sub-areas and explore different approaches aimed at reducing the environmental foot print and operating costs of multi-site Internet services, while honoring the Quality of Service (QoS) constraints as contracted in service level agreements (SLAs). Firstly, we model the load distribution among member DCs of a multi-site Internet service. The use of green energy is optimized considering different factors such as (a) geographically and temporally variable electricity prices, (b) the multitude of available energy sources to choose from at each DC, (c) the necessity to support more than one SLA, and, (d) the requirements to offer more than one service at each DC. Various approaches are presented for solving this problem and extensive simulations using Google’s setup in North America are used to evaluate the presented approaches. Secondly, we explore the area of shaving the peaks in the energy demand of large electricity consumers, such as DCs by using a battery-based energy storage system. Electrical demand of DCs is typically peaky based on the usage cycle of their customers. Resultant peaks in the electrical demand require development and maintenance of a costlier energy delivery mechanism, and are often met using expensive gas or diesel generators which often have a higher environmental impact. To shave the peak power demand, a battery can be used which is charged during low load and is discharged during the peak loads. Since the batteries are costly, we present a scheme to estimate the size of battery required for any variable electrical load. The electrical load is modeled using the concept of arrival curves from Network Calculus. Our analysis mechanism can help determine the appropriate battery size for a given load arrival curve to reduce the peak. Thirdly, we present techniques to employ intra-DC scheduling to regulate the peak power usage of each DC. The model we develop is equally applicable to an individual server with multi-/many-core chips as well as a complete DC with an intermix of homogeneous and heterogeneous servers. We evaluate these approaches on single-core and multi-core chip processors and present the results. Overall, our work demonstrates the value of model-based design for intelligent load distribution across DCs, storage integration, and per DC optimizations for efficient energy management to reduce operating costs and environmental footprint for multi-site Internet services

Coupling Life Cycle Assessment and Socioeconomic Scenarios for Climate Change Adaptation of the Energy-Water Nexus

The interdependency of water and energy resources is known as energy-water-nexus (EWN). Water is necessary for energy production and energy is integral to water acquisition and distribution. The carbon emissions associated with both water and energy sectors drive climate change. Climate change in return poses increasing stress on the energy water nexus and makes tradeoffs between resources necessary and increasingly challenging, given the constraints and uncertainty around resources. This dissertation focuses on the tradeoffs between greenhouse gas mitigation and water conservation in the energy-water-nexus and how adaptation policy can influence these tradeoffs.To quantitatively understand these tradeoffs especially under future development pathways, a modeling framework is developed to first develop socioeconomic storylines that contain local information around energy water nexus, and a life cycle assessment model that quantifies the energy and water footprints for an energy system based on input data assessing various policy and technology pathways. In this dissertation, such a framework is developed and tested and applied in the context of shale gas production in Barnett Texas.Three collaborative research manuscripts developed for this dissertation are presented as three chapters following an Introduction and summed up with a Conclusion. Chapter 1 develops sub-national and sectoral extensions of the global shared socioeconomic pathways (SSPs), as nested qualitative storylines, in order to identify future socioeconomic challenges for adaptation for the United States on national, regional and local scales. Chapter 2 develops a life-cycle assessment (LCA) model to evaluate the global warming potential and water scarcity footprints associated with multiple wastewater management options associated with shale gas production in the Barnett Shale play of Texas. Chapters 3 combines the two frameworks developed in Chapters 1 and 2, by testing the nested SSPs for Texas, by developing shared policy assumptions and quantifying them as input parameters to the LCA model, to evaluate energy and technology pathways around adaptation of hydraulic fracturing and water use in Texas. The Conclusion synthesizes the main findings from the three chapters and discusses opportunities to use the research to improve future policy decisions related to climate change and energy-water nexus