The U.S. fruit and vegetable processing industry--defined in this Energy Guide as facilities engaged in the canning, freezing, and drying or dehydrating of fruits and vegetables--consumes over $800 million worth of purchased fuels and electricity per year. Energy efficiency improvement is an important way to reduce these costs and to increase predictable earnings, especially in times of high energy price volatility. There are a variety of opportunities available at individual plants in the U.S. fruit and vegetable processing industry to reduce energy consumption in a cost-effective manner. This Energy Guide discusses energy efficiency practices and energy-efficient technologies that can be implemented at the component, process, facility, and organizational levels. A discussion of the trends, structure, and energy consumption characteristics of the U.S. fruit and vegetable processing industry is provided along with a description of the major process technologies used within the industry. Next, a wide variety of energy efficiency measures applicable to fruit and vegetable processing plants are described. Many measure descriptions include expected savings in energy and energy-related costs, based on case study data from real-world applications in fruit and vegetable processing facilities and related industries worldwide. Typical measure payback periods and references to further information in the technical literature are also provided, when available. Given the importance of water in fruit and vegetable processing, a summary of basic, proven measures for improving plant-level water efficiency are also provided. The information in this Energy Guide is intended to help energy and plant managers in the U.S. fruit and vegetable processing industry reduce energy and water consumption in a cost-effective manner while maintaining the quality of products manufactured. Further research on the economics of all measures--as well as on their applicability to different production practices--is needed to assess their cost effectiveness at individual plants.

The term ?household carbon footprint? refers to the total annual carbon emissions associated with household consumption of energy, goods, and services. In this project, Lawrence Berkeley National Laboratory developed a carbon footprint modeling framework that characterizes the key underlying technologies and processes that contribute to household carbon footprints in California and the United States. The approach breaks down the carbon footprint by 35 different household fuel end uses and 32 different supply chain fuel end uses. This level of end use detail allows energy and policy analysts to better understand the underlying technologies and processes contributing to the carbon footprint of California households. The modeling framework was applied to estimate the annual home energy and supply chain carbon footprints of a prototypical California household. A preliminary assessment of parameter uncertainty associated with key model input data was also conducted. To illustrate the policy-relevance of this modeling framework, a case study was conducted that analyzed the achievable carbon footprint reductions associated with the adoption of energy efficient household and supply chain technologies.

This paper summarizes a modeling framework that characterizes the key underlying technologies and processes that contribute to the supply chain energy use and greenhouse gas (GHG) emissions of a variety of goods and services purchased by U.S. consumers. The framework couples an input-output supply chain modeling approach with "bottom-up" fuel end use models for individual IO sectors. This fuel end use modeling detail allows energy and policy analysts to better understand the underlying technologies and processes contributing to the supply chain energy and GHG "footprints" of goods and services. To illustrate the policy-relevance of this approach, a case study was conducted to estimate achievable household GHG footprint reductions associated with the adoption of best practice energy-efficient supply chain technologies.

Personal computers (PCs) are one of the most ubiquitous and indispensable electronic devices in use within California’s homes and businesses. More PCs are estimated to be in use in California than in any other U.S. state. According to the latest published estimates from U.S. Department of Energy (DOE), 11.5 million PCs were installed in California’s homes (as of 2001) and 6.5 million PCs were installed in California’s commercial buildings (as of 2003) (U.S. DOE 2003, 2006a). Based on these DOE data and recent PC sales data, it was estimated in this research project that as of 2005 nearly 19 million PCs were installed in California homes and businesses and that this number is expected to grow significantly through 2012.

This research project focused on evaluating a set of realistic measures aimed at reducing the life-cycle energy use and GHG emissions associated with operating and maintaining California’s residential and commercial PC stock (hereafter referred to simply as “California’s PC stock”). The term “maintain” is used here to describe the ongoing process of replacing, upgrading, and discarding obsolete PCs within California’s PC stock as necessary. The project employed a life-cycle assessment (LCA) approach to characterize both direct and indirect energy use and GHG emissions. Direct energy use and GHG emissions were defined as the energy use and GHG emissions attributable to the operational electricity consumption of California’s PC stock. Indirect energy use and GHG emissions were defined as the energy use and GHG emissions attributable to activities that support ongoing PC stock maintenance, namely, the manufacture of new PCs and PC components as needed and the end-of-life treatment (i.e., waste disposal and recycling) of obsolete PCs and PC components.

In this research project, the potential reductions in life-cycle energy use and GHG emissions associated with the measures considered were projected through the year 2012. The purpose of this work was to characterize the effectiveness of these measures to inform PC-related policies for near-term energy efficiency improvements and GHG emission reductions in the State of California. Baseline projections were first established for the life-cycle energy use and GHG emissions associated with operating and maintaining California’s PC stock from 2005 to 2012. These baseline projections were made using best-available data from public sources for all key modeling parameters. Whenever possible, ranges for key modeling parameters were established to account for published data variations. Two future scenarios were considered: a low energy use and GHG emissions scenario (“low scenario”) and a high energy use and GHG emissions scenario (“high scenario”). The purpose of the two-scenario approach was to establish preliminary upper and lower bounds on the results based on the feasible data ranges identified from the literature for key modeling parameters. The potential reductions in life-cycle energy use and GHG emissions associated with the measures considered were then projected for each scenario over the same time period.

Commercial PC estimate was derived by: (1) assuming that 8.6 million PCs were installed in commercial buildings in the Pacific Census Region in 2003, based on the U.S. DOE 2003 Commercial Building Energy Use Survey (CBECS) (U.S. DOE 2006), and (2) assuming that 75% of Pacific Census Region commercial sector employment (as defined by the 2003 CBECS) and hence PC usage occurs in California based on employment data from the U.S. Census Bureau (U.S. Census Bureau 2006).