4
Energy Efficiency in Industry

Building on improvements in energy efficiency in U.S. industrial manufacturing that have occurred over the past several decades in response to volatile fossil-fuel prices, fuel shortages, and technological advances is essential to maintaining U.S. industry’s viability in an increasingly competitive world. The fact is that many opportunities remain to incorporate cost-effective, energy-efficient technologies, processes, and practices into U.S. manufacturing. This chapter describes the progress made to date and the magnitude of the untapped opportunities, which stem both from broader use of current best practices and from a range of possible advances enabled by future innovations. It focuses on the potential for improving energy efficiency cost-effectively in four major energy-consuming industries—chemical manufacturing and petroleum refining, pulp and paper, iron and steel, and cement—and discusses the role of several crosscutting technologies as examples. In addition, this chapter identifies major barriers to the deployment of energy-efficient technologies, outlines the business case for taking action to improve the energy efficiency of U.S. manufacturing, and presents the associated findings of the Panel on Energy Efficiency Technologies.

4.1
ENERGY USE IN U.S. INDUSTRY IN A GLOBAL CONTEXT

As shown in Chapter 1, Figure 1.1, industry is responsible for 31 percent of primary energy use in the United States. Figure 4.1 illustrates how this energy use was distributed among industries, particularly the most energy-intensive ones, in 2004.



The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 185
4 Energy Efficiency in industry B uilding on improvements in energy efficiency in U.S. industrial manufactur- ing that have occurred over the past several decades in response to volatile fossil-fuel prices, fuel shortages, and technological advances is essential to maintaining U.S. industry’s viability in an increasingly competitive world. The fact is that many opportunities remain to incorporate cost-effective, energy-efficient technologies, processes, and practices into U.S. manufacturing. This chapter describes the progress made to date and the magnitude of the untapped opportuni- ties, which stem both from broader use of current best practices and from a range of possible advances enabled by future innovations. It focuses on the potential for improving energy efficiency cost-effectively in four major energy-consuming industries—chemical manufacturing and petroleum refining, pulp and paper, iron and steel, and cement—and discusses the role of several crosscutting technologies as examples. In addition, this chapter identifies major barriers to the deployment of energy-efficient technologies, outlines the business case for taking action to improve the energy efficiency of U.S. manufacturing, and presents the associated findings of the Panel on Energy Efficiency Technologies. 4.1 ENERGY USE IN U.S. INDUSTRY IN A GLOBAL CONTEXT As shown in Chapter 1, Figure 1.1, industry is responsible for 31 percent of pri- mary energy use in the United States. Figure 4.1 illustrates how this energy use was distributed among industries, particularly the most energy-intensive ones, in 2004. 8

OCR for page 185
8 Real Prospects for Energy Efficiency in the United States Chemicals 7.8 Transportation Aluminum 0.9 28% Petroleum Refining 7.3 Industry 33% Fabricated Metals 0.7 Forest Products 3.3 Plastic and Rubber 0.7 Commercial Iron and Steel 1.9 18% Food Processing 1.6 Nonmetallic Minerals 1.4 Residential 21% Nonmanufacturing 4.1 Other Manufacturing 3.8 FIGURE 4.1 Total energy use in the U.S. industrial sector in 2004, quadrillion Btu (quads). Values include electricity-related losses. Total U.S. energy use in 2004 was 100.4 quads; total U.S. industrial energy use in 2004 was 33.6 quads. 4.1 Efficiency Source: Craig Blue, Oak Ridge National Laboratory, based on EIA (2004) (preliminary) and estimates extrapolated from EIA (2002). Globally, industry is the largest consumer of energy—the energy that it con- sumes exceeds that devoted to transportation, the residential sector, and commer- cial buildings combined. According to the International Energy Outlook 00, the industrial sector worldwide used 51 percent of the total delivered energy (or 50 percent of the primary energy) in the year 2006, and its demand was pro- jected to grow by an annual rate of 1.4 percent between 2006 and 2030 (EIA, 2009a).1 Before 1973, manufacturing was the largest energy consumer in most member countries of the Organisation for Economic Cooperation and Develop- ment (OECD), but in recent years its dominance has subsided as industrial output has slowed, energy efficiency has increased, and other sectors have surged ahead (Schipper, 2004). As a result, industrial energy demand in OECD countries was anticipated to grow only 0.6 percent annually. In contrast, industrial-sector energy 1See http://www.eia.doe.gov/oiaf/ieo/excel/ieoendusetab_1.xls.

OCR for page 185
Energy Efficiency in Industry 8 consumption in non-OECD countries was projected to increase by 2.1 percent per year over the same period, with the most rapid growth occurring in China and India. As of 2006, industry accounted for 33 percent of the primary energy con- sumed in the United States and 28 percent of carbon dioxide (CO2) emissions (EIA, 2008). Overall, the quantity of energy used by U.S. industries is huge, esti- mated at 32.6 quadrillion British thermal units (quads) of primary energy in 2006 at a cost of $205 billion. About 5 quads, or 21 percent of this total, was for non- fuel uses of coal, gas, and oil—for example, the use of oil refining by-products in asphalt, natural gas employed as a feedstock for petrochemicals, and petroleum coke used in the production of steel (EIA, 2009b). U.S. industries use more energy than the total energy used by any other Group of Eight (G8) nation and about half of the total energy used by China (DOE, 2007b). The average annual rate of growth of energy in the U.S. industrial sector is projected to be 0.3 percent out to 2030, while CO2 emissions from U.S. industry are projected to increase more slowly, at 0.2 percent annually (EIA, 2008). These low rates are due partly to the presumed introduction of energy-efficient tech- nologies and practices in industry. They also reflect the projected restructuring of the economy away from energy-intensive manufacturing and toward service and information-based activities. Many of the commodities that were once produced in the United States are now manufactured offshore and imported into the country. The energy embodied in these imported products is not included in the standard energy metrics published by the Energy Information Administration (EIA) of the Department of Energy (DOE). According to an analysis by Weber (2008), prod- ucts imported into the United States in 2002 had an embodied energy content of about 14 quads, far surpassing the embodied energy of exports from the United States (about 9 quads). The most energy-intensive manufacturing industries are those producing metals (iron, steel, and aluminum); refined petroleum products; chemicals (basic chemicals and intermediate products); wood and glass products; mineral products such as cement, lime, limestone, and soda ash; and food products. As shown in Figure 4.1, these industries are responsible for more than 70 percent of industrial energy consumption. Industries that are less energy-intensive include the manu- facture or assembly of automobiles, appliances, electronics, textiles, and other products.

OCR for page 185
88 Real Prospects for Energy Efficiency in the United States 4.1.1 Recent Trends in Industrial Energy Use Primary-energy use in the industrial sector declined in the 1970s following the run-up of energy prices. Energy consumption bottomed out in the mid-1980s and then increased steadily through the turn of the century, exceeding its previous peak. Table 4.1 shows energy use for selected years within this period (excluding nonfuel uses). In recent years, industrial energy use has declined partly as a result of the economic restructuring noted above. Energy use in the manufacturing sector continues to be significantly higher than in the nonmanufacturing sectors, which include agriculture, forestry and fisheries, mining, and construction. Energy-use trends in some sectors have been relatively stable, such as in chemical manufactur- TABLE 4.1 Total U.S. Industrial Energy Use (Excluding Nonfuel Uses of Coal, Oil, and Natural Gas), in Selected Years from 1978 to 2004 (in quadrillion Btu) Usea 1978 1985 1990 1995 2002 Food Manufacturing, Beverage, and Tobacco (311/312) 1.36 1.4 1.35 1.72 1.77 Textile Mills, Textile Mill Products (313/314) 0.53 0.43 0.46 0.54 0.44 Apparel, Leather and Allied Products (315/316) 0.19 0.10 0.11 0.16 0.66 Wood Product Manufacturing (321) 0.64 0.52 0.59 0.67 0.70 Paper Manufacturing (322) 2.38 2.66 3.16 3.17 3.14 Printing and Related Support Activities (323) 0.16 0.15 0.20 0.22 0.23 Petroleum and Coal Products Manufacturing (324) 3.09 2.01 3.37 3.37 3.92 Chemical Manufacturing (325) 4.20 3.05 4.22 4.22 4.06 Plastic and Rubber Products Manufacturing (326) 0.45 0.44 0.52 0.67 0.86 Nonmetallic Mineral Product Manufacturing (327) 1.62 1.16 1.29 1.23 1.32 Primary Metal Manufacturing (331) 5.01 2.43 2.73 2.74 2.70 Fabricated Metal Product Manufacturing (332) 0.66 0.58 0.65 0.75 0.72 Machinery Manufacturing (333) 0.50 0.38 0.43 0.44 0.39 Computer and Electronic Product Manufacturing (334) 0.29 0.39 0.47 0.47 0.38 Electrical Equipment, Appliance, and Component 0.24 0.23 0.29 0.34 0.27 Manufacturing (335) Transportation Equipment (336) 0.73 0.66 0.70 0.77 0.82 Furniture and Related Product Manufacturing (337) 0.12 0.09 0.12 0.13 0.14 Miscellaneous Manufacturing (339) 0.14 0.11 0.12 0.14 0.17 Total (Manufacturing) 22.3 16.8 20.8 21.7 22.1 Total (Non-Manufacturing) Not 6.0 4.8 5.5 3.3 available aNorth American Industry Classification System codes are given in parentheses. Totals may not equal sum of components due to independent rounding. Source: U.S. Department of Energy, U.S. Energy Intensity Indicators, Trend Data, Industrial Sector. Available at http://www1. eere.energy.gov/ba/pba/intensityindicators/.

OCR for page 185
Energy Efficiency in Industry 8 TABLE 4.2 Primary Energy Consumption by Type of Fuel in the U.S. Industrial Sector (quadrillion Btu, or quads) 1978 1985 1990 1995 2002 Petroleum 9.87 7.74 8.28 8.61 9.57 Natural gas 8.54 7.08 8.50 9.64 8.67 Coal 3.31 2.76 2.76 2.49 2.03 Renewable energy 1.43 1.91 1.67 1.91 1.68 Source: U.S. Department of Energy, U.S. Energy Intensity Indicators, Trend Data, Industrial Sector. Available at http://www1. eere.energy.gov/ba/pba/intensityindicators/. ing and wood product manufacturing. In other sectors, however, energy use has increased significantly. For example, energy use in the plastic and rubber products manufacturing sector almost doubled between 1978 and 2002. Petroleum and natural gas are the two most common fuels consumed by the industrial sector (Table 4.2). While the use of petroleum and natural gas increased by 24 and 22 percent, respectively, from 1985 to 2002, coal consumption dropped by approximately 27 percent. The use of renewable energy has fluctuated over the years, totaling 1.43 quads in 1978, rising to 1.91 quads in 1985, and then retreat- ing to 1.68 quads in 2002. 4.1.2 Energy-Intensity Trends and Comparisons Between 1985 and 2003, industrial-sector gross domestic product (GDP) increased by 64 percent, while industrial energy use increased by only 12 percent (Figure 4.2), resulting in a significant decline in the energy intensity of the indus- trial sector (DOE/EERE, 2008). As previously noted, over the past decade struc- tural factors (the change in manufacturing output relative to industrial output and the shift among manufacturing sectors to less energy-intensive industries) have caused a decline in energy intensity and in total industrial energy use. By comparing the energy intensity of manufacturing across 13 countries that are members of the International Energy Agency (IEA), Schipper (2004, p. 18) provides a glimpse into the relative efficiency of U.S. manufacturing. A simple comparison of manufacturing energy use per dollar of output suggests that the United States has a slightly higher than average manufacturing energy intensity. This is corroborated by statistics from the IEA (2004, p. 69) on energy use per unit of manufacturing value added in countries that are members of the OECD.

OCR for page 185
0 Real Prospects for Energy Efficiency in the United States 1.8 1.6 1.4 Index (1985 = 1.0) 1.2 1.0 0.8 GDP in Industry GDP 0.6 Energy Use Inten 0.4 Structure Struc Intensity Ener 0.2 0.0 1985 1987 1989 1991 1993 1995 1997 1999 2001 2003 Year FIGURE 4.2 Trends in U.S. industrial sector gross domestic product (GDP), energy use, structure, and energy intensity, 1985–2003. Industrial GDP increased 64ficiency 4.2 Ef percent between 1985 and 2003; energy intensity (energy use per dollar of GDP) declined by 19 percent over the same period, with most of the decline occurring since 1993. “Structure” rep- resents the change in manufacturing as a fraction of total industrial output, and the changes that have occurred within manufacturing. Manufacturing, which is more energy-intensive than nonmanufacturing, has seen a growth in GDP relative to total industrial GDP, with most of that change occurring since 1995. This factor has added about 6 percent to energy use, most of this effect occurring after the recession in the early 1990s. Manufacturing industries that are less energy- intensive have grown relative to those manufacturing industries that are highly energy- intensive, thus reducing the energy intensity of manufacturing as a whole. Source: DOE/EERE, 2008. The United States is considered a country with medium energy intensity country along with Finland, Sweden, and the Netherlands. High-energy-intensity countries include Norway, Australia, and Canada. At the same time, the United States has a less energy-intensive manufacturing sectoral structure relative to the other 12 IEA member countries, many of which are big producers of raw materials (e.g., Aus- tralia, Canada, the Netherlands, Norway, and Finland).2 Correcting for this differ- ence raises the U.S. energy-intensity index compared with that of other IEA coun- 2Taking into account the activity of multinational corporations headquartered in each country.

OCR for page 185
Energy Efficiency in Industry  TABLE 4.3 “Business as Usual” Forecast of U.S. Industrial Energy Consumption (quadrillion Btu, or quads) Industry 2006 2020 2030 Refining 3.94 6.07 7.27 Aluminum 0.39 0.36 0.33 Iron and steel 1.44 1.36 1.29 Cement 0.45 0.43 0.41 Bulk chemical 6.83 6.08 5.60 Paper 2.18 2.31 2.49 Total 32.6 34.3 35 Source: EIA, 2008a. tries. While the analysis by Schipper is based on somewhat dated statistics (focus- ing on 1994), the panel’s assessment is that its fundamental conclusion regarding the relative energy inefficiency of U.S. manufacturing remains valid. The EIA’s Annual Energy Outlook 00 forecasted that U.S. industrial energy consumption would increase from approximately 34.1 quads in 2006 to 35.8 quads in 2020 and 38.7 in 2030 (EIA, 2007). This baseline forecast assumed the continuation of current policies and some autonomous, or naturally occurring, efficiency improvement (see Section 4.2.1.4). The EIA’s Annual Energy Outlook 008 reduced the 2007 forecast’s pro- jected increase in U.S. industrial energy consumption substantially to reflect the nation’s economic slowdown, rising energy prices, and the passage of the Energy Independence and Security Act of 2007 (P.L. 110-140) (EIA, 2008). With rising prices and more policy levers encouraging energy efficiency, greater energy effi- ciency improvement is anticipated to occur naturally as part of the 2008 baseline forecast. Specifically, the 2008 EIA estimate of U.S. industrial energy consumption for 2006 is 32.6 quads, 34.3 quads for 2020, and 35.0 quads for 2030 (Table 4.3). With a lower anticipated rate of growth in energy consumption, the potential for further cost-effective efficiency improvements must be recalibrated. This has been done by scaling the percentage savings for 2007 to the 2008 projections (see Section 4.2.1.1).

OCR for page 185
 Real Prospects for Energy Efficiency in the United States 4.2 POTENTIAL FOR ENERGY SAVINGS 4.2.1 Review of Studies of Energy Efficiency Potential Two major studies that have attempted to assess the potential for cost-effective energy efficiency improvements across the U.S. industrial sector—Scenarios for a Clean Energy Future (IWG, 2000) and The Untapped Energy Efficiency Opportu- nity in the U.S. Industrial Sector (McKinsey and Company, 2007)—are described below. In addition, many studies have examined the potential for energy efficiency in individual manufacturing industries such as aluminum, chemicals, and paper; others have focused on the potential impact of specific technologies (such as membranes or combined heat and power [CHP]) or families of technologies (e.g., sensors and controls, fabrication and materials). Such cross-sectional studies are the subject of Section 4.3 (focusing on major energy-consuming industries) and Section 4.4 (focusing on crosscutting technologies and processes). Because they do not treat the industrial sector comprehensively, these studies do not enable a sector-wide estimation of economic energy efficiency potential. However, they provide valuable benchmarking of the two comprehensive studies discussed below. In addition, there are state-level and international assessments of industrial energy efficiency potential, which are also drawn on below. 4.2.1.1  U.S. Industrial-Sector Assessments In the DOE-sponsored study Scenarios for a Clean Energy Future (CEF), pre- pared by the Interlaboratory Working Group on Energy-Efficient and Clean Energy Technologies (IWG), a portfolio of advanced policies3 was estimated to reduce energy consumption in the industrial sector by 16.6 percent relative to a business-as-usual (BAU) forecast, at no net cost to the economy (IWG, 2000; see also Brown et al., 2001, and Worrell and Price, 2001). The assumptions made in the study regarding cost-effectiveness are detailed in Box 4.1. The policies were assumed to be implemented in the year 2000; the 16.6 percent reduction was the difference between the BAU forecast for 2020 and the scenario trajectory 3The effects of many policies for reducing energy use and greenhouse gas emissions from in- dustry are modeled in Scenarios for a Clean Energy Future (IWG, 2000). These include industry- wide agreements to reduce greenhouse gas emissions, the expanded deployment and marketing of ENERGY STAR® buildings, the rapid expansion of industrial energy assessment programs, and a carbon cap-and-trade system.

OCR for page 185
Energy Efficiency in Industry  BOX 4.1 Cost-Effectiveness of Industrial Energy Efficiency Investments Investment decisions can be characterized by the internal rate of return (IRR), also called the hurdle rate, used to trigger an expenditure. The IRR involves a dis- counted cash flow analysis that is based on a firm’s cost of capital plus or minus a risk premium to reflect the project’s particular risk profile. McKinsey and Company (2007, 2008) assumes that investments with an IRR greater than 10 percent are cost-effective. In their studies, each investment opportunity is treated individually; no integrated analysis is conducted to determine whether investments in one tech- nology might impact the economics of other investment options. The CEF study, Scenarios for a Clean Energy Future (IWG, 2000), does not use a single hurdle rate. Rather, it draws on a variety of best-in-class modeling approaches that employ economic metrics seen as appropriate to particular sectors and technologies. For example, in the buildings sector, the business-as-usual hurdle rate is assumed to be about 15 percent (in real terms). In the advanced scenario, the potential impact of individual policies on energy demand was assessed in detailed spreadsheets using lower discount rates (typically about 7 percent), reflecting the influence of supporting policies that remove barriers to the adoption of energy- efficient technologies. The hurdle rates and other parameters inside the buildings- sector modules of the National Energy Modeling System (NEMS; the energy modeling system used by the U.S. Department of Energy’s Energy Information Administration) were then changed so that the model replicates the energy sav- ings calculated from the CEF spreadsheets (IWG, 2000). In the industrial sector, the business-as-usual hurdle rate was generally assumed to be approximately 30 percent. In the advanced scenario, industrial subsectors were assessed using a hurdle rate of 15 percent to reflect the impact of the policy instruments that reduce transaction costs and financial risks. Combined heat and power (CHP) was modeled separately using Resource Dynamics Corporation’s DISPERSE model because of limitations of the NEMS model (IWG, 2000). As a final step, the NEMS integration model was used to assess the full range of effects of the economy-wide technology and policy scenarios. The integration step allows technology trade-offs and allows the effects of changes in energy use in each sector to be taken into account in the energy-use patterns of other sectors (IWG, 2000). in 2020 as defined by advanced policies. The annual energy cost savings from the advanced scenario was estimated to exceed the sum of the annualized policy implementation costs and the incremental technology investments. (See Box 4.2 for further description of the CEF study.)

OCR for page 185
 Real Prospects for Energy Efficiency in the United States Box 4.2 The Scenarios for a Clean Energy Future Study The study Scenarios for a Clean Energy Future (CEF; IWG, 2000) was conducted by scientists at five U.S. Department of Energy (DOE) national laboratories with more than $1 million in funding from the DOE and the U.S. Environmental Protection Agency. Published in November 2000, it involved a comprehen- sive analysis of U.S. technology and policy opportunities, using a combination of engineering-economic analysis and a modified version of the DOE Energy Information Administration’s National Energy Modeling System (CEF-NEMS). In the study the major sectors of the economy (buildings, industry, transportation, and electricity) were analyzed separately to identify the most cost-effective energy policy and technology alternatives for addressing multiple energy-related chal- lenges facing the nation. Using CEF-NEMS, an integrated assessment of technology and policy options was produced. Seven supplemental studies are published in the CEF report’s 600-page appendix (e.g., an assessment of combined heat and power opportunities). The appendix also contains details of the engineering-economic analysis so as to enable full public disclosure and replication by others. The report had extensive peer review, including that of a blue-ribbon advisory committee, and the results were the subject of a special issue of Energy Policy published in 2001 (see Brown et al., 2001). Taken from the Annual Energy Outlook , the BAU forecast used in the CEF study (IWG, 2000) estimated that the U.S. industrial sector would require 41.2 quads of energy in 2020. In contrast, the advanced portfolio of policies (defined earlier in the CEF study and assumed implemented by 2020) produced a scenario with industry requiring only 34.3 quads of energy (saving 6.9 quads of energy, a 16.6 percent reduction). The 2008 EIA projection (EIA, 2008) fore- casts a BAU industrial-sector consumption of only 34.3 quads of energy in 2020. Scaling the 16.6 percent savings estimate to this lower level of future baseline industrial energy consumption suggests a savings of 5.7 quads, or a possible pol- icy-induced reduction in industrial energy use to 28.4 quads.4 These sector-wide 4When the panel applies older estimates of percentage improvement to newer (and lower) BAU estimates of energy to estimate the absolute energy savings, it is possible to create double- counting even if there was no double-counting in the original estimate of percentage improve- ment. That is, some of the energy efficiency improvements in the original estimate may have become a part of the BAU forecast (partially explaining the reduction in the BAU). The panel expects this problem to be negligible or nonexistent, because new energy efficiency opportunities

OCR for page 185
Energy Efficiency in Industry  savings estimates do not account for the possible efficiencies available from CHP systems, because at the time of the Annual Energy Outlook , the model used by the EIAthe National Energy Modeling Systemwas unable to model CHP technology in an integrated manner. The Scenarios for a Clean Energy Future study commissioned an off-line analysis of the economic energy-savings potential of new CHP under “advanced” policies. This assessment concluded that CHP could reduce the energy require- ments of the industrial sector by 2.4 quads in 2020 (IWG, 2000; Lemar, 2001). Scaling this estimate to reflect the downward forecast of future industrial energy consumption suggests an economic savings potential of 2 quads.5 In combination with the sector’s other energy efficiency opportunities identified in the CEF study, this brings the total estimate of economic energy-savings potential to 7.7 quads, or 22.4 percent of the Annual Energy Outlook 008 (EIA, 2008) forecasted con- sumption of 34.3 quads in 2020. Building on the CEF study, on other assessments, and on original research, a more recent publication by McKinsey and Company (2007)6 concurred that U.S. industries have a significant opportunity for energy efficiency gains (Figure 4.3). Financially attractive investments (defined as those with internal rates of return [IRRs] of 10 percent or greater) are estimated to offer 3.9 quads in energy-usage reduction in 2020, compared with the business-as-usual forecast based on the ref- erence case of the Annual Energy Outlook 00 (EIA, 2007). These investments are estimated by McKinsey and Company (2007) to generate $30–$55 billion in increased earnings, before interest and taxes, by 2020; this earnings growth would, in turn, generate a $210–$385 billion increase in the market value of industrial companies. As shown in Figure 4.3, an additional 1.0 quad is identified by McK- insey and Company (2007) as “additional opportunities through driving R&D,” bringing the estimated energy efficiency potential in the industrial sector to 4.9 arise each year as infrastructure and equipment age and as new and improved technologies are introduced into the marketplace. 5The EIA 1998 forecast of industrial energy consumption in 2020 was 41.2 quads, and the EIA 2008 forecast is 34.3. Multiplying 2.4 quads times the ratio of these two forecasts (0.83) results in the estimated 2.0 quad savings from the use of CHP. 6The McKinsey and Company study has been widely criticized for ignoring adoption and transaction costs and the potential impacts on product attributes. For example, it does not in- clude the cost of policy or program implementation, as is done in great detail in the CEF study (IWG, 2000; see Appendix E-1 of that study).

OCR for page 185
0 Real Prospects for Energy Efficiency in the United States and artificially inflates the value of the dirtiest plants. Altogether, these effects have led some critics to question whether the NSR program and the NSPS have resulted in higher levels of pollution than would have occurred in the absence of regulation (Brown and Chandler, 2008). 4.6 THE BUSINESS CASE FOR ENERGY EFFICIENCY Other than subsidies, regulation, and supporting public policies, what might moti- vate industry to improve its energy efficiency? What are the most important lever- age points for motivating efficiency improvements? Some of the most important of these drivers are described below. • Rising energy prices. The sustained pain of rising oil, coal, natural gas, and electricity prices is motivating a renewed interest in energy effi- ciency. To remain competitive, industry must find ways to reduce its energy consumption, and higher energy costs can make efficiency invest- ments more cost-competitive. Of course, the cost of efficiency invest- ments can also rise with energy costs (perhaps lagged by a few years). Thus, the excitement over finally being able to justify an alternative-fuel or energy-reduction project because of recent energy cost increases is often dampened by the discovery of an accompanying rise in the cost of equipment and materials. • Environmental concerns and regulations. Many states are allowing industry to use energy efficiency to qualify for NOx and SO2 offsets in non-attainment zones. With a lowering of acceptable ozone concentra- tions, many additional counties in the United States are going to be in non-attainment. Title IV SO2 allowances are now trading at less than $100/ton, and NOx is trading at less than $1000/ton. At higher prices, these allowances could provide a lucrative stream of payments for many industrial efficiency investments.20 Most energy policy analysts forecast 20The average weighted price for a ton of SO2 in 2009 was $69.74 (http://www.epa.gov/ airmarkt/trading/2009/ 09summary.html). The average price for a ton of NOx in March 2009 was roughly $625/ton (seasonal) (http://www.ferc.gov/ market-oversight/othr-mkts/emiss-allow/ othr-emns-no-so-pr.pdf).

OCR for page 185
Energy Efficiency in Industry  that there will be tradable allowances for CO2 sometime in the next several years. • Demand charges and demand-response incentives. The ability of indus- try to cut peak electric loads is a motivator for utilities to incentivize demand response (shifting loads to off-peak periods) in industry. Indus- trial energy efficiency measures that reduce energy demand (or slow its growth) can also help utilities meet energy needs, so promoting such savings can be in the utilities’ interest. In combination with peak-load pricing for electricity, energy efficiency and demand response can be a lucrative enterprise for industrial customers. • Collateral benefits. Secondary or collateral benefits such as increased productivity, improved product quality, reduced labor costs, and enhanced reliability are often strong drivers for energy efficiency improvements (Worrell et al., 2003). This was illustrated effectively in Cool Companies: How the Best Companies Boost Profit and Produc- tivity by Cutting Greenhouse Gas Emissions (Romm, 1999), which describes the many ways that corporations have benefited from increas- ing the energy efficiency of their operations. • International competition. If a company cannot sell its products because of the cost of the energy needed to produce them relative to the costs of domestic or international competitors, attention may turn to energy efficiency improvements in the manufacturing process. • Corporate sustainability. Voluntarily reducing greenhouse gas emissions and implementing climate change mitigation strategies offer ways to boost shareholder and investor confidence, profit from future legisla- tion, access new markets, lower insurance costs, avoid liability, offer competitive benefits, and prevent and prepare for physical and market damage caused by further climate impact. Almost all of the Fortune 500 companies are publishing corporate responsibility reports. Many com- panies are setting energy efficiency goals (e.g., Johnson and Johnson, BP, Exxon, Dupont). Similarly, ISO 14000 certification informs the pub- lic about the nature of the production processes and is being required by DOE, Dow, and others. • Shareholder activism, good corporate governance, and reputation management are other potential drivers of energy efficiency in indus- try. ENERGY STAR® designations and other government programs that recognize outstanding environmental performance by corporate

OCR for page 185
 Real Prospects for Energy Efficiency in the United States America have proven to be strong motivators of resource and energy conservation. • Insurance access and costs, legal compliance, and concerns regarding fiduciary duty are additional business case drivers for managing green- house gas emission reductions through energy efficiency (Natural Edge Project, 2005). 4.7 FINDINGS The following findings derive from the panel’s analysis of industrial efficiency summarized in this chapter. I.1 Independent studies using different approaches agree that the economic potential for improved energy efficiency in industry is large. Of the 34.3 quads of energy forecasted to be consumed by U.S. industry in 2020 (EIA, 2008), 14–22 percent could be saved through cost-effective energy efficiency improvements (those with an internal rate of return of at least 10 percent or that exceed a company’s cost of capital by a risk pre- mium). These innovations would save 4.9–7.7 quads annually by 2020. I.2 Additional efficiency investments could become cost-competitive through energy RD&D. Enabling and crosscutting technologies, such as advanced sensors and controls, microwave processing of materials, nanoceramic coatings, and high-temperature membrane separation, can provide efficiency gains in many industries as well as throughout the energy system—for example, in vehicles, feedstock conversion, and elec- tricity transmission and distribution. I.3 Industry has experienced a significant shift to offshore manufacturing of components and products. If the net energy embodied in imports and exports is taken into account, the energy consumption attributable to industry would be increased by 5 quads. I.4 Energy-intensive industries such as aluminum, steel, and chemicals have devoted considerable resources to increasing their energy efficiency. For many other industries, energy represents 10 percent or less of costs and is not a priority. Energy efficiency investments compete for human and financial resources with other goals such as increased production, improved productivity, introduction of new products, and compliance

OCR for page 185
Energy Efficiency in Industry  with environment, safety, and health requirements. Outdated capital depreciation schedules, backup fees for combined heat and power sys- tems, and other policies also hamper energy efficiency investment. I.5 More detailed data, collected more frequently, are needed to better assess the status of and prospects for energy efficiency in industry. Pro- prietary concerns will have to be addressed to achieve this. I.6 Drivers for energy efficiency in industry include rising and volatile energy prices, intense competitive pressure to lower costs, and an increased focus on corporate sustainability. 4.8 REFERENCES AFPA (American Forest and Paper Association). 2007. Talking Points for Key Provisions Opposed by AF&PA in H.R. 3221. 2007. Available at http://www. bipac.net/page.asp?g=afpa&content=one-pager-Talking_Points_for_House_Energy_ Bill&parent=AFPA. AISI (American Iron and Steel Institute). 2005. Saving One Barrel of Oil per Ton. Washington, D.C.: AISI. October. AISI. 2006. Annual Statistical Report. Washington, D.C.: AISI. American Chemistry Council. 2008. Guide to the Business of Chemistry. Available at http://www.americanchemistry.com/store/detail.aspx?ID=243. ANL (Argonne National Laboratory). 1990. Environmental Consequences of, and Control Processes for, Energy Technologies. Pollution Technology Review Series, No. 181. Norwich, N.Y.: William Andrew. Bailey, O., and E. Worrell. 2005. Clean Energy Technologies: A Preliminary Inventory of the Potential for Electricity Generation. Report LBNL-57451. Berkeley, Calif.: Lawrence Berkeley National Laboratory. September. Banerjee, R., A. Phan, B. Wang, C. Knobler, H. Furukawa, M. O’Keeffe, and O.M. Yaghi. 2008. High-throughput synthesis of zeolitic imidazolate frameworks and application to CO2 capture. Science 319:939-943. Battelle (Battelle Memorial Institute). 2002. Toward a Sustainable Cement Industry: Climate Change, Substudy 8, An Independent Study Commissioned by the World Business Council for Sustainable Development. Columbus, Ohio: Battelle Memorial Institute. March. Berry, R.S, V.A. Kazakov, S. Sieniutycz, Z. Szwast, and A.M. Tsirlin. 1999. Thermodynamic Optimization of Finite-Time Processes. Chichester, U.K.: John Wiley & Sons.

OCR for page 185
 Real Prospects for Energy Efficiency in the United States Brooks, S., B. Elswick, and R.N. Elliott. 2006. Combined Heat and Power: Connecting the Gap Between Markets and Utility Interconnection and Tariff Practices (Part 1). Washington, D.C.: American Council for an Energy-Efficient Economy. March. Brown, M., and S. Chandler. 2008. Governing confusion: How statutes, fiscal policy, and regulations impede clean energy technologies. Stanford Law and Policy Review 19(3):472-509. Brown, M., J. Chandler, M. Lapsa, and B. Sovacool. 2008. Carbon Lock-In: Barriers to Deploying Climate Change Mitigation Technologies. Report TM-2007/124. Oak Ridge, Tenn.: Oak Ridge National Laboratory. Available at http://www.ornl.gov/sci/ btc/pdfs/brown_doc7435_tm124_ 08.pdf. Brown, M., M. Levine, W. Short, and J. Koomey. 2001. Scenarios for a clean energy future. Energy Policy 29(14):1179-1196. Canepa, A., and P. Stoneman. 2004. Comparative international diffusion: Patterns, deter- minants and policies. Economics of Innovation and New Technology 13(3):279-298. Casten, T., and R. Ayres. 2007. Energy myth eightWorldwide power systems are economically and environmentally optimal. Pp. 201-238 in Energy and American SocietyThirteen Myths (B.K. Sovacool and M.A. Brown, eds.). New York: Springer. Chapman, S., G. Leslie, and I.B. Law. 2004. Membrane bioreactors (MBR) for municipal wastewater treatmentAn Australian perspective. Journal of the Australian Water Association 4:65. Cowart, R. 2001. Efficient Reliability: The Critical Role of Demand-Side Resources in Power Systems and Markets. Report to the National Association of Regulatory Utility Commissioners by the Regulatory Assistance Project. Available at http://www. raponline.org/Pubs/General/EffReli.pdf. DOE (Department of Energy). 2002. Industrial Wireless Technology for the 21st Century. Industrial Wireless Workshop, San Francisco, July 30, 2002. Washington, D.C.: DOE, Office of Energy Efficiency and Renewable Energy. DOE. 2004. Energy Use, Loss, and Opportunities Analysis: U.S. Manufacturing and Mining. Prepared by Energetics Incorporated and E3M Incorporated. Washington, D.C.: DOE, Industrial Technologies Program. December. Available at http://www1. eere.energy.gov/industry/energy_systems/analysis.html. DOE. 2005a. Energy and Environmental Profile of the U.S. Pulp and Paper Industry. Washington, D.C.: DOE, Industrial Technologies Program. DOE. 2005b. U.S. Climate Change Technology ProgramTechnology Options for the Near and Long Term. Washington, D.C.: DOE, Climate Change Technology Program. August. Available at http://www.climatetechnology.gov.

OCR for page 185
Energy Efficiency in Industry  DOE. 2006a. Chemical Bandwidth Study: Energy Analysis: A Powerful Tool for Identifying Process Inefficiencies in the U.S. Chemical Industry. Draft Summary Report. Study conducted for the U.S. Department of Energy by JVP International, Incorporated, and Psage Research, LLC. Summary report prepared by Energetics Incorporated. Washington, D.C.: DOE. December. Available at http://www1.eere.energy.gov/industry/ chemicals/bandwidth.html. DOE. 2006b. Energy Bandwidth for Petroleum Refining Processes. Prepared by Energetics Incorporated. October. Washington, D.C.: DOE. Available at http://www1.eere.energy. gov/industry/petroleum_refining/bandwidth.html. DOE. 2006c. Pulp and Paper Industry Energy Bandwidth Study. Prepared by Jacobs Engineering Group and Institute of Paper Science and Technology for the American Institute of Chemical Engineers and DOE. Washington, D.C.: DOE. August. Available at http://www1.eere.energy.gov/industry/forest/bandwidth.html. DOE. 2006d. Strategic Plan. Climate Change Technology Program. Washington, D.C.: DOE. September. DOE. 2007a. Energy and Environmental Profile of the U.S. Petroleum Refining Industry. Prepared by Energetics Incorporated. Washington, D.C.: DOE, Industrial Technologies Program. November. Available at http://www1.eere.energy.gov/industry/petroleum_ refining/analysis.html. DOE. 2007b. Energy Technology Solutions: Public-Private Partnerships Transforming Production. Washington, D.C.: DOE, Energy Efficiency and Renewable Energy. October. DOE. 2008. U.S. Steel Industry Energy Efficiency Fact Sheet. Washington, D.C.: Climate Vision Partnership, Sector Initiatives: Iron and Steel. Available at http://www. climatevision.gov/sectors/steel/index.html. August 7. DOE/EERE (Department of Energy/Office of Energy Efficiency and Renewable Energy). 2005. Infrared Heating Technology: Crosscutting Applications, Widespread Benefits. March. Available at http://www1.eere.energy.gov/industry/imf/pdfs/ irheatingmarch05. pdf. DOE/EERE. 2007. IMPACTSIndustrial Technologies Program: Summary of Program Results for CY 2005. Washington, D.C.: Department of Energy, Office of Energy Efficiency and Renewable Energy. February. Available at http://www1.eere.energy.gov/ industry/ about/pdfs/impact2005.pdf. DOE/EERE. 2008. Energy Intensity Indicators in the U.S., Highlights of Trends, Industrial Total Energy Consumption. April 14. Available at http://www1.eere.energy.gov/ba/pba/ intensityindicators/total_industrial.html. EIA (Energy Information Administration). 1998. Annual Energy Outlook 1998. Washington, D.C.: Department of Energy, Energy Information Administration.

OCR for page 185
 Real Prospects for Energy Efficiency in the United States EIA. 1999. Annual Energy Outlook 1999. Washington, D.C.: Department of Energy, Energy Information Administration. EIA. 2002. 2002 Manufacturing Energy Consumption Survey. Washington, D.C.: Department of Energy. Available at http://www.eia.doe.gov/emeu/efficiency/mecs_ trend_9802/ mecs_trend9802.html. EIA. 2004. Monthly Energy Review. Washington, D.C.: Department of Energy. Available at http://www.eia.doe.gov/emeu/mer/contents.html. EIA. 2007. Annual Energy Outlook 2007. DOE/EIA-0383(2007). Washington, D.C.: Department of Energy, Energy Information Administration. EIA. 2008. Annual Energy Outlook 2008. DOE/EIA-0383(2008). Washington, D.C.: Department of Energy, Energy Information Administration. EIA. 2009a. International Energy Outlook. Washington, D.C.: Department of Energy, Energy Information Administration. EIA. 2009b. Monthly Energy Review (July). Washington, D.C.: Department of Energy, Energy Information Administration. Expert Group on Energy Efficiency. 2007. Realizing the Potential of Energy Efficiency: Targets, Policies, and Measures for G8 Countries. United Nations Foundation Expert Report. Washington, D.C. Fruehan, R.J. 2008. Future Steelmaking Processes. Materials Science and Engineering Department, Carnegie Mellon University. Available at http://www.osti.gov/bridge/ servlets/purl/840930-yRKP7T/webviewable/840930.PDF. Fruehan, R., O. Fortini, H. Paxton, and R. Brindle. 2000. Theoretical Minimum Energies to Produce Steel for Selected Conditions. Report prepared by Carnegie Mellon University for the Department of Energy. March. Available at http://www1.eere.energy. gov/industry/steel/pdfs/theoretical_minimum_energies.pdf. Goldman, C., Lawrence Berkeley National Laboratory. 2006. Utility Experience with Real-Time Pricing. Presentation at the Workshop on Smart Meters and Time-Based Rates, Montepelier, Vt., March 15. Available at http://www.state.vt.us/psb/document/ ElectricInitiatives/GoldmanVermontPSBpricingworkshop031506.pdf/. IDA (International Desalination Association). 1995. Proceedings of International Desalination Association World Congress, International Desalination and Water Reuse Conference, Abu-Dhabi, United Arab Emirates, 1995. Topsfield, Mass.: IDA. IEA (International Energy Agency). 2004. Oil Crises and Climate Challenges30 Years of Energy Use in IEA Countries. Paris: Organisation for Economic Cooperation and Development (OECD)/IEA. IEA. 2007. Tracking Industrial Energy Efficiency and CO2 Emissions. Paris, France: IEA. IEA. 2009. Cogeneration and District Energy: Sustainable Energy Technologies for Today and Tomorrow. Paris, France: IEA. Available at http://www.iea.org/files/ CHPbrochure09.pdf.

OCR for page 185
Energy Efficiency in Industry  IPCC (Intergovernmental Panel on Climate Change) 2007. Climate Change 2007: Mitigation of Climate Change. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. B. Metz, O.R. Davidson, P.R. Bosch, R. Dave, L.A. Meyer, eds. Cambridge, United Kingdom, and New York: Cambridge University Press. IWG (Interlaboratory Working Group on Energy-Efficient and Clean Energy Technologies). 2000. Scenarios for a Clean Energy Future. ORNL/CON-476 and LBNL-44029. Oak Ridge National Laboratory and Lawrence Berkeley National Laboratory. November. Jimenez, E.S, P. Salamon, R. Rivero, C. Rendon, K.H. Hoffmann, M. Schaller, and B. Andresen. 2004. Optimization of a diabatic distillation column with sequential heat exchangers. Industrial and Engineering Chemistry Research 43:7566-7571. KEMA, Inc. 2006. California Industrial Existing Construction Energy Efficiency Potential Study, Volumes 1 and 2. Final Report to Pacific Gas and Electric Company. Prepared with assistance from Lawrence Berkeley National Laboratory and Quantum Consulting. Arnhem, The Netherlands: KEMA, Inc. May. King, D. 2006. Electric Power Micro-grids: Opportunities and Challenges for an Emerging Distributed Energy Architecture. Doctoral Thesis. Pittsburgh, Pa.: Carnegie Mellon University. May. Laitner, J.A., and M.A. Brown. 2005. Emerging industrial innovations to create new energy efficient technologies. In Proceedings of the Summer Study on Energy Efficiency in Industry. Washington, D.C.: American Council for an Energy-Efficient Economy. Lave, L.B. 2009. The potential of energy efficiency: An overview. The Bridge 39(2):5-14. LBNL (Lawrence Berkeley National Laboratory). 2005. Energy Efficiency Improvement and Cost Saving Opportunities for Petroleum Refineries, An ENERGY STAR® Guide for Energy and Plant Managers. LBNL-57260-Revision. Prepared by C. Galitsky, S. Chang, E. Worrell, and E. Masanet. Berkeley, Calif.: LBNL. February. Lemar, P.L. 2001. The potential impact of policies to promote combined heat and power in U.S. industry. Energy Policy 29(14):1243-1254. Lin, H., E. Van Wagner, B.D. Freeman, L.G. Toy, and R.P. Gupta. 2006. Plasticization- enhanced hydrogen purification using polymeric membranes. Science 311:639-642. Lovins, A.B. 2007. Energy myth nineEnergy efficiency improvements have already reached their potential. Pp. 239-264 in Energy and American SocietyThirteen Myths. B.K. Sovacool and M.A. Brown, eds. New York: Springer. Martin, N., N. Anglani, D. Einstein, M. Khrushch, E. Worrell, and L.K. Price. 2000a. Opportunities to Improve Energy Efficiency and Reduce Greenhouse Gas Emissions in the U.S. Pulp and Paper Industry. LBNL-46141. Berkeley, Calif.: Lawrence Berkeley National Laboratory.

OCR for page 185
8 Real Prospects for Energy Efficiency in the United States Martin, N., W. Worrell, M. Ruth, L. Price, R.N. Elliott, A.M. Shipley, and J. Thorne. 2000b. Emerging Energy-Efficient Industrial Technologies. Berkeley, Calif.: Lawrence Berkeley National Laboratory. October. McKinsey and Company. 2007. The Untapped Energy Efficiency Opportunity in the U.S. Industrial Sector. Report prepared for DOE, Office of Energy Efficiency and Renewable Energy. New York: McKinsey and Company. McKinsey and Company. 2008. The Untapped Energy Efficiency Opportunity of the U.S. Industrial Sector: Details of Research, 2008. New York: McKinsey and Company. Mullins, O.C., and R.S. Berry. 1984. Minimization of entropy production in distillation. Journal of Physical Chemistry 88:723-728. NRC (National Research Council). 2007. Prospective Evaluation of Applied Energy Research and Development at DOE (Phase Two). Washington, D.C.: The National Academies Press. NREL (National Renewable Energy Laboratory). 2002. Chemical Industry of the Future: Resources and Tools for Energy Efficiency and Cost Reduction Now. Report DOE/ GO-102002-1529 and NREL/CD-840-30969. October. Available at http://www.nrel. gov/docs/fy03osti/30969.pdf. Natural Edge Project. 2005. Prospering in a Carbon Constrained World. Queensland, Australia: Natural Edge Project. May. Nenoff, T.M., M. Ulutagay-Kartin, R. Bennett, K. Johnson, G. Gray, T. Anderson, M. Arruebo, R. Noble, J. Falconer, X. Gu, and J. Dong. 2006. Novel Modified Zeolites for Energy-Efficient Hydrocarbon Separations. SAND2006-6891. Albuquerque, N.Mex.: Sandia National Laboratories. Optimal Energy, Inc. 2003. Energy Efficiency and Renewable Energy Resource Development Potential in New York State. Final Report. Prepared for the New York State Energy Research and Development Authority. August. Available at http://www. nyserda.org/energy_Information/otherdocs.asp#EERER. Orlov, V.N., and R.S. Berry. 1991. Estimation of minimal heat consumption for heat driven separation processes via methods of finite-time thermodynamics. Journal of Physical Chemistry 95:5624-5628. ORNL (Oak Ridge National Laboratory) and BCA, Inc. 2005. Materials for Separation Technologies: Energy and Emission Reduction Opportunities. Washington, D.C.: Department of Energy, Office of Energy Efficiency and Renewable Energy. Available at http://www.eere.energy.gov/industry/imf/analysis.html. Pace Energy Project. 2002. Combined Heat and Power Market Potential in New York State. Prepared for the New York State Energy Research and Development Authority, Albany, N.Y. May. Available at http://www.icfi.com/markets/energy/doc_files/eea-chp- ny.pdf.

OCR for page 185
Energy Efficiency in Industry  Romm, J.J. 1999. Cool Companies: How the Best Companies Boost Profit and Productivity by Cutting Greenhouse Gas Emissions. New York, N.Y.: Island Press. Schaller, M., K.H. Hoffmann, G. Siragusa, P. Salamon, and B. Andresen. 2001. Optimized performance in diabatic distillation columns. Computers and Chemical Engineering 25:1537-1548. Schipper, L. 2004. International comparisons of energy end use: Benefits and risks. Pp. 1- 27 in Encyclopedia of Energy. Volume 3. C.J. Cleveland, ed. Amsterdam: Elsevier. Shipley, A., A. Hampson, B. Hedman, P. Garland, and P. Bautista. 2008. Combined Heat and Power: Effective Energy Solutions for a Sustainable Future. ORNL/TM-2008/224. Oak Ridge, Tenn.: Oak Ridge National Laboratory. December. Available at http:// www1.eere.energy.gov/industry/distributedenergy/. Sieniutycz, S., and P. Salamon, eds. 1990. Finite-Time Thermodynamics and Thermoeconomics, Advances in Thermodynamics. Volume 4. New York: Taylor and Francis. Sovacool, B.K., and R.F. Hirsh. 2007. Energy myth six: The barriers to new and innovative energy technologies are primarily technical: The case of distributed generation (DG). Pp. 145-169 in Energy and American Society—Thirteen Myths. B.K. Sovacool and M.A. Brown, eds. New York: Springer. Thorp IV, B.A., and L.D. Murdock-Thorp. 2008. Compelling case for integrated biorefin- eries. Paper presented at the 2008 PAPERCON Conference, May 4-7, Dallas, Texas. Norcross, Ga.: Technical Association of the Pulp and Paper Industry. U.S. Census Bureau. 2007. 2006 Annual Survey of Manufacturers (ASM). Data available at http://www.census.gov/mcd/asmhome.html. van Oss, H.G. 2005. Background Facts and Issues Concerning Cement and Cement Data. Open-File Report 2005-1152. Washington, D.C.: U.S. Department of the Interior, USGS. Weber, C. 2008. Energy Use and Flows in the U.S. Economy, 1997-2002. Prepared for the National Academy of Sciences by Carnegie Mellon University, Pittsburgh, Pa. August. Worrell, E., and G. Biermans. 2005. Move over! Stock turnover, retrofit, and industrial energy efficiency. Energy Policy 33:949-962. Worrell, E., and C. Galitsky. 2004. Energy Efficiency Improvement and Cost Saving Opportunities for Cement MakingAn ENERGY STAR® Guide for Energy and Plant Managers. LBNL-54036. Berkeley, Calif.: Lawrence Berkeley National Laboratory. Worrell, E., and M. Neelis. 2006. World’s Best Practice Energy Intensity Values for Selected Industrial Sectors. LBNL-62806. Berkeley, Calif.: Lawrence Berkeley National Laboratory. Worrell, E., and L. Price. 2001. Policy scenarios for energy efficiency improvement in industry. Energy Policy 29(14):1223-1241. Worrell, E., C. Galitsky, and L. Price. 2008. Energy Efficiency Improvement Opportunities for the Cement Industry. Berkeley, Calif.: Lawrence Berkeley National Laboratory. January.

OCR for page 185
0 Real Prospects for Energy Efficiency in the United States Worrell, E., J. Laitner, M. Ruth, and H. Finman. 2003. Productivity benefits of industrial energy efficiency measures. Energy, the International Journal 28(11):1081-1098. Worrell, E., L. Price, and C. Galitsky. 2004. Emerging Energy-Efficient Technologies in Industry: Case Studies of Selected Technologies. LBNL-54828. Berkeley, Calif.: Lawrence Berkeley National Laboratory. May. Worrell, E., L. Price, N. Martin, C. Hendriks, and L.O. Meida. 2001. Carbon diox- ide emissions from the global cement industry. Annual Review of Energy and the Environment 26:303-329. Wright, A., M. Martin, B Gemmer, P. Scheihing, and J. Quinn. 2007. Results from the U.S. DOE 2006 Save Energy Now Assessment initiative: DOE’s Partnership with U.S. Industry to Reduce Energy Consumption, Energy Costs, and Carbon Dioxide Emissions. ORNL/TM-2007/138. Oak Ridge, Tenn.: Oak Ridge National Laboratory. WSA (World Steel Association) Statistical Archive. 2008. Available at http://www. worldsteel.org/?action=stats_search. Accessed December 2, 2008. Xenergy, Inc. 1998. United States Industrial Electric Motor Systems Market Opportunities Assessment. Washington, D.C.: Department of Energy, Office of Energy Efficiency and Renewable Energy. Available at http://www1.eere.energy.gov/industry/bestpractices/ pdfs/mtrmkt.pdf.