Environ. Sci. Technol. 2010, 44, 7335–7346
Comparative Assessment of Life Cycle Assessment Methods Used for Personal Computers MARISSA A. YAO,* TIM G. HIGGS, MICHAEL J. CULLEN, SCOTT STEWART, AND TODD A. BRADY Intel Corporation, 2200 Mission College Boulevard, Santa Clara, California 95054
Received October 30, 2009. Revised manuscript received July 9, 2010. Accepted July 20, 2010.
This article begins with a summary of findings from commonly cited life cycle assessments (LCA) of Information and Communication Technology (ICT) products. While differing conclusions regarding environmental impact are expected across product segments (mobile phones, personal computers, servers, etc.) significant variation and conflicting conclusions are observed even within product segments such as the desktop Personal Computer (PC). This lack of consistent conclusions and accurate data limits the effectiveness of LCA to influence policy and product design decisions. From 1997 to 2010, the majority of published studies focused on the PC concluded that the use phase contributes most to the life cycle energy demand of PC products with a handful of studies suggesting that manufacturing phase of the PC has the largest impact. The purpose of this article is to critically review these studies in order to analyze sources of uncertainty, including factors that extend beyond data quality to the models and assumptions used. These findings suggest existing methods to combine process-based LCA data with product price data and remaining value adjustments are not reliable in conducting life cycle assessments for PC products. Recommendations are provided to assist future LCA work.
examination of these findings reveals issues which extend beyond data quality to include the scenarios, models, and study designs used by researchers. The remainder of this paper will focus on the life cycle energy and CO2 impacts (total energy consumed by the device over its expected lifespan and the estimated CO2 emissions that result from that energy use) of desktop PCs and on key factors that influence the results, especially a) usage scenario assumptions and b) models that are based on PC price data and ‘remaining value’ calculations. By examining the assumptions and methods used in previous analyses, the intent is to identify the significant causes of variation and error. It is essential for policymakers and researchers to understand that the lack of complete and up-to-date life cycle and environmental economic data has led to the use of ad-hoc methods, such as the use of remaining value that do not appear to work well for this sector. Variable and inaccurate results do not further the knowledge base for ICT LCA but rather hinder efforts to collectively identify priority areas for research, product design, and policy-making. Findings from this analysis reaffirm the continuing need for improved life cycle data, standardized methods for ICT life cycle assessment, error analysis, and peer review.
Discussion Introduction Multiple studies have attempted to quantify the environmental footprint of various Information and Communication Technology (ICT) products using variations of the Life Cycle Assessment (LCA) method. Table 1 lists a sample of ICTfocused LCA studies published since 1997. A wide range of ICT products and environmental impacts have been evaluated, and many of these studies have focused specifically on the energy and CO2 life cycle impacts. From Life Cycle Inventories to Hybrid Life Cycle Assessments, researchers have struggled with challenges inherent to both the LCA method and the ICT sector itself. Not surprisingly, conclusions are often incomparable mainly due to varying boundary conditions, assumptions, and data sources even within the same product segment (mobile phones, personal computers, servers, etc.) This lack of consistent conclusions and accurate data limits the effectiveness of LCA to influence policy and product design decisions. For example, the majority of published studies that focused on the PC concluded that the use phase contributes most to the life cycle energy demand of PC products with a handful of studies suggesting that manufacturing phase of the PC has the largest impact. Closer * Corresponding author e-mail:
[email protected]. 10.1021/es903297k
2010 American Chemical Society
Published on Web 08/31/2010
1. Usage Scenarios. Estimating the total amount of energy consumed through the use of a PC product depends on three factors: device power consumption, consumer usage patterns, and product lifetimes (4). Examining the variations in these factors help explain the difference in usage impacts results (15). For the study authored by Choi et al., the annual usage patterns (time spent in active versus inactive modes per year) appear to be the most significant factor followed by the assumed power consumption. Choi and his coauthors concluded that the use phase contributed less than 20% to the Global Warming impact of a PC; the manufacturing of PC components including the main board, hard disk drive, and other subcomponents was defined by the authors as “pre-manufacturing” and accounted for over 80% of a PC’s global warming potential (15). One of the complications associated with predicting PC usage patterns and energy use is the lack of common terminology among authors as it relates to the multiple PC power states. However, in recent years, four general power states have emerged: operational mode, idle mode, sleep, and off. In order to compare Choi et al.’s usage pattern with other studies, it will be assumed that “operational” will include “Min”, “Max”, and “Active” states. Similarly, “idle” will include “sleep” but not the “off” state. Compared to office usage patterns cited by other studies, the use pattern VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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product
personal computer
personal computer
pager
monitor
wireless network/ system
computer tape drive
semiconductor
Internet
personal computer
personal computer
personal computer
wireless network/ system
personal computer
personal computer
year
1997
1998
1998
1999
2000
2000
2001
2002
2003
2004
2004
2005
2005
2006
Braune and Warburg (IKP)
Hikwama
Scharnhorst et al.
Williams
Williams
Williams
Loerincik et al.
Taiariol et el.
Chambers and Matthews
Weidman and Lundberg
Kim et al.
Scheller and Hoffman
Atlantic Consulting and IPU
Tekawa et al.
authors
title
Revisiting Energy Used To Manufacture a Desktop Computer: Hybrid Analysis Combining Process and Economic Input-Out Methods (10) Energy Intensity of Computer Manufacturing: Hybrid Assessment Combining Process and Economic Input-Output Models (11) The End of Life Treatment of Second Generation Mobile Phone Networks: Strategies To Reduce the Environmental Impact (12) Life Cycle Assessment of a Personal Computer (13) EPIC-ICTL Development of Environmental Performance Indicators for ICT Products on the Example of Personal Computers (14)
Use vs Manufacture Life Cycle Electricity and Environmental Impacts for Computer Tape Drives (6) Life Cycle Assessment of an Integrated Circuit Product (7) Life Cycle Environmental Impacts of the Internet (8) Computers and the Environment (9)
Life Cycle Assessment of a Telecommunication Product (Pager) (3) LCA Study of Color Computer Monitor (4) Life Cycle Assessment of Ericsson Third Generation Systems (5)
Life Cycle Assessment: An Approach to Environmentally Friendly PCs (1) LCA Study of the Product Group Personal Computers in the EU Ecolabel Scheme (2)
TABLE 1. Sample of ICT Life Cycle Assessments (LCA) Published from 1997-2010
use
use
use
production
production
production
use
use
use
use
use
material extraction
use
use
phase that contributes most to energy impact
Figure 6-2B shows that the largest contributions of impact come from the use phase. (p 50) Use stage contributes more than 50% to Primary Energy (MJ) and Global Warming Potential (kg CO2 equivalents) for typical Office PC + CRT monitor (p 11)
The results indicate that the environmental impacts attributable to the use phase dominate the environmental impacts incurred over the entire life cycle of the network. (p 540)
In contrast with many home appliances, life cycle energy use of a computer is dominated by production (81%) as opposed to operation (19%). (p 6166)
The production of a computer takes a lion’s share of total energy use in the product life cycle -80% versus 20% for electricity to run the computer. (p 7) In contrast with many home appliances, life cycle energy use of a computer is dominated by production (83%) as opposed to operation (17%). (p 80)
For both PCs, the greenhouse effect index in the use stage was the largest and was second largest in the production stage. (pp 127-128) The characterized data for global warming show that the largest contributions come from the use stage where they are caused by the electricity consumption during use. The second largest contributor is the manufacture stage, but it is nearly three times lower than the contribution from use. (p 37) Material extraction phase has the main share of impacts. Production and use phase contribute also significantly. (p 308) This study shows that the use phase is the most contributing phase throughout the life cycle. (p 42) It was determined that the most significant environmental impacts of the systems studied were associated with energy use, both in the use of telecom equipment by the operators and the use of networks by end consumers. Manufacturing of Ericsson equipment accounted for less than 20% of the total impact of the wireless system. (p 138) When viewed from a life cycle perspective, electronic products, like many other products such as automobiles, are more energy intensive over the use phase than manufacture. (p 14) Table 2: Total Gross Energy for EPROM Device: 70.85 MJ; Use phase: 58.35 or 82% of total (p 132) Use phase is in most of the cases dominating
results
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personal computer
semiconductor
semiconductor
2009
2010
semiconductor
2008
2009
personal computer
2007
semiconductor
personal computer
2007
2009
wireless network/ system
2006
semiconductor
personal computer
2006
2008
product
year
TABLE 1. Continued
Boyd et al.
Boyd et al.
Shah et al.
Higgs et al.
Hermanns and O’Cleirigh
IVF Industrial Research and Development Corporation Krishnan et al.
Eugster et al.
Scharnhorst et al.
Choi et al.
authors
Life-Cycle Assessment of Computational Logic Produced from 1995 through 2010 (24)
Life-Cycle Energy Demand and Global Warming Potential of Computational Logic (23)
Developing an Overall CO2 Footprint for Semiconductor Products (21) Assessing ICT’s Environmental Impact (22)
Emerging Methods for Scope 3 Greenhouse Gas Accounting (20)
Preparatory Studies for Eco-Design Requirements of EuPs Lot 3: Personal Computers (18) A Hybrid Life Cycle Inventory of Nano-Scale Semiconductor Manufacturing (19)
Life Cycle Assessment of a Personal Computer and Its Effective Recycling Rate (15) Life Cycle Assessment of Second Generation (2G) and Third Generation (3G) Mobile Phone Networks (16) Key Environmental Impacts of the Chinese EEE-Industry (17)
title
use
use
use
use
use
use
use
manufacturing, use
use
material extraction/ pre-manufacturing
phase that contributes most to energy impact
For example, looking at Figure 2a, impact metrics such as global warming potential and energy use are highest during the operational stage. (p 93) For all technology generations, the use phase represents the largest proportion of energy-related impacts per die among the lifecycle phases. (p 7305) Use-phase electricity consumption generates the majority of life-cycle GWP impacts at all nodes, with an increasing share over time. (p 3)
Given that 442 Pentium 4 Northwood processors are produced per wafer, we determine use phase energy to be 93-124 GJ/wafer. This quantity is almost a factor of 8 larger than our total upstream production chain energy estimate. (p 3074) Principally speaking it can be expected that the GHG emissions from the use phase of AMD products are higher than those of its manufacture, when assuming a certain average usage pattern. (p 317) Impact from product use is more than ten times larger than the impact from operations. (p 4)
The LCA study of the desktop PC system shows that the phases manufacturing and use generate very high environmental impacts. In the use phase, about six times more energy is used than in any other phase. (p 159)
The premanufacturing stage was a significant stage for all of the environmental parameters, besides human toxicity potential. (p 122) The use phase (i.e., the operation) of the radio network components account for a large fraction of the total environmental impact. (p 656)
results
TABLE 2. Comparison of Office Usage Patterns for Desktop Personal Computers office PC assumptions: 5 days/week 52 weeks/yr
operational mode (hours/week) idle/sleep mode (hours/week) kWh/year product lifetime (years)
Williams
EPA
Dell
average service life for first owner average service life for used PC total
0
10
140 3
231 4
32.5 194 4
established regions
emerging regions
4.4 years
4.6 years
1.4 years
1.5 years
5.8 years
6.1 years
assumed by Choi et al. results in significantly lower energy and CO2 impact attributed to the use phase. In this model, the total annual electricity consumption amounts to 76 kWh. By comparison, the next lowest yearly estimate shown in Table 2 is 140 kWh - a 2-fold increase from Choi’s calculation. Given the conservative usage pattern used by Choi et al., it is likely that the relative contribution of the use phase is underestimated in this study. Studies also vary in the assumed product lifetimes of PCs with a range of 2.5 years to 6.6 years. As one would expect, the number of years assumed for a product’s lifetime will proportionally impact the estimated cumulative energy consumption. The majority of studies use a product lifetime of 3 or 4 years. The study developed by IVF for the EcoDesign Requirements for Energy Using Products (EuP) assumed a product lifetime of 6.6 years (18). For the 2009 Intel study, slightly different assumptions were made about the use and recycle rate for desktop PCs in established regions vs emerging regions. Similar to the definition used by Eugster et al., the “effective” lifetime used by the authors includes second users of the device (17). The assumptions are shown in Table 3 with product lifetime ranging between 5.8 years and 6.1 years (21). 2. Life Cycle Assessment. Among the most well-known and persistent challenges to conducting a life cycle assessment is the amount of data required. Compounding this is the lack of readily available, accurate data across the supply chain. For personal computers, this problem is particularly challenging due to rapid process changes, global supply chains, frequent product design changes, and shorter product lifetimes when compared to other products and services (22). The lack of lifecycle data for the supply chain is the most frequently cited reason for the varying scopes and boundary conditions. Practitioners are often forced to adjust boundary conditions depending on the data available. This “truncation” of boundary conditions is a widely acknowledged limitation of existing process-analysis LCAs because it limits the scope to a defined area, often excluding upstream processes and embedded materials from the analysis (26-28). Input Output Lifecycle Assessment. Input Output (IO) Lifecycle Assessment is a method designed to address the gaps and weaknesses stemming from limited boundary conditions (truncation) and lack of supply chain data (26). The methods and assumptions for input output (IO) LCA are 9
Eugster
EuP
Intel
TIAX
(10) (14) (14) (15) (17) (18) (21) (23) PC incl. CRT PC only, excl. PC only, excl. PC only, excl. PC incl. CRT PC only, excl. PC only, excl. PC only, excl. monitor monitor monitor monitor monitor monitor monitor monitor 21 37.5 43.5 12.9 29 44 32 56.8
TABLE 3. Lifetime Assumptions for Desktop Personal Computers (PCs)
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8.95 76 4
9.69 250 6
43.2
136
3.7
178 6.6
192 6
230 N/A
described in greater detail elsewhere (26-29). The principal goals of IO LCA are to enable users to conduct an assessment with a wider scope in less time. By looking across industrial sectors, the IO method attempts to avoid truncation errors by including entire economies within a study’s scope and accounting for such factors as business travel, transportation, etc. Reviewers have observed that results of IO-LCAs are generally higher than process analysis LCAs (8, 30). However, the accuracy of several studies that employ the IO LCA method is debatable given the wide ranges in variation. Suh et al. observe “impacts based only on input-output analysis showed large variations from -80% to +125% compared to the process-based result” (28). For products such as PCs, the range of variability is similarly wide with one study citing a range of -32% - +300% (11). This inconsistency illustrates the need to better understand the key determining factors that render certain industries and products better suited to IO LCA and other industries and products more compatible with process LCA methods. Although some may simply attribute the variation to the wider scope enabled by IOLCA, critical factors have been identified which weaken IOLCA results for computing equipment (such as personal computers), including the following: Age of Data. Until August 2008, the IO tables most often used in LCA studies were based on 1997 data. Consequently, IO-LCA experts acknowledge that “IO coefficients for rapidly changing industries (e.g., computer manufacturing, which has rapid development of products and processes) may be very different over time” (29). In an industry driven by Moore’s Law, which predicts an exponential increase in the complexity of integrated circuits, a decade represents multiple technology shifts. This is illustrated by Figure 1 which shows the trend in electricity consumption by the semiconductor manufacturing industry. According to the World Semiconductor Council, the amount of electricity used to manufacture each square centimeter of silicon decreased by approximately 37% between 2001 and 2008. Since IO tables are updated once
FIGURE 1. kWh per cm2 silicon: 2001-2008. Source World Semiconductor Council.
TABLE 4. Summary Findings of 3 Hybrid LCA Studies for Desktop Personal Computersa
FIGURE 2. Components used in analyses published in 2004 (10, 11).
every five years and have a six-year time lag (the most recently published tables in 2008 are based on 2002 data), significant and rapid manufacturing efficiencies characteristic of certain industries will not be reflected in IO tables. Single Region IO Table. Since most of the existing IO-LCA studies referenced in Table 1 are based on US tables, the accuracy and representativeness of the results are limited since “imports are implicitly assumed to have the same production characteristics as comparable products made in the country of interest” (27, 29). For the computer manufacturing industry, whose supply chain and manufacturing process span the globe, the effect of this limitation cannot be overlooked. As Suh explains, “results of IO analyses of countries that rely heavily on imports are subject to a relatively high uncertainty” (28). If prices are assumed to be correlated to environmental impact (e.g., energy intensity), then it is likely that the IO results will be subject to high variability and inaccuracy if data from only one region is used. Indeed, the lack of compatible, global IO tables has been identified as a contributing factor to the wide range of variation noted in previous estimates for desktop PCs: -32% - +300% (11). Aggregation. IO-LCA is described as offering data for over 400 industrial sectors as classified by the North American Industry Classification System (NAICS). However, as acknowledged by the developers of the main IO based LCA tool (EIO-LCA), the “data do not directly map onto the IO sectors in the economic models” (29). Since data are presented at the industrial sector level, granularity is limited, thereby making it difficult for users to differentiate specific products and materials (31). For the PC manufacturing industry and its supply chain, this has also been cited as a main cause of uncertainty, especially for electronic chemicals since “there is no clear choice of IO sector that matches these activities closely” (11). Linear Model. The input-output method also assumes that impact or resource consumption is directly proportional to the price of a product. As others have stated, the assumption is “the effects of a $1,000 purchase from a sector will be ten times greater than the effects of a $100 purchase from the same sector” (27, 42). Economic activity may be related to environmental activity at the macrolevel, and it could thus be assumed that higher economic activity brings with it additional environmental impacts. But the magnitude and linearity of the correlation are debatable. Numerous factors and characteristics including pricing structures, manufacturing processes, technology development, and design optimizations render specific conclusions or predictions difficult. The challenge is compounded if the focus moves from the macrolevel (e.g., industry sector) to the microlevel (e.g., product or service level). On the outset, one can assume a correlation between economic activity and environmental impact would be more likely with industries and products that do not experience significant or frequent changes in process, design, or price. While this assumption may work for commodities, specialized products which include a high degree of intellectual property in the final price or products whose prices experience significant fluctuations do not fit this model well. The potential disadvantages of using a linear model are discussed in the next section. Subsequent analysis will show product
computers and the env (9) PC price year average PC price ($) process LCA (MJ) input output (MJ) remaining value ($) total energy (MJ) total fossil fuels (kg)
1998 1100 5040-9600 5040, 5600, 9600 240
revisiting energy used to manuf energy intensity a desktop of computer computer (10) manuf (11) 2000 1700 3100-3200 1100 3100 7300 290
2000 1700 3100 1100 2100 6400 260
a
Figures were derived by summing original data and rounding to two significant figures.
price cannot be used to accurately or precisely quantify the energy required to manufacture a product. The limitations associated with price data are further amplified when combined with remaining value calculations as will be discussed in the following section. Hybrid Economic Input Output Lifecycle Assessment. Given the challenges associated with both process-analysis and input-output LCA, researchers have continued to seek alternative methods to quantify the environmental impacts of products and services. The Hybrid LCA approach attempts to address the gaps in both process LCA and IO LCA methods. By combining process LCA data with additive economic correction factors, the intent of hybrid LCA is to arrive at a “complete” environmental profile. However, the way in which the two approaches are combined is key. As the following analysis will show, incorrect use of process LCA with economic data and additional components such as remaining value can lead to inaccurate results. As suggested by EIOLCA experts, researchers should carefully identify those areas in which EIO-LCA data are suitable and to use process LCA data for those “processes not expected to follow the IO-LCA model” (42). For example, process LCA data can be used for the product’s manufacture and use, while EIO-LCA can be used for aspects such as distribution, packaging, and disposal. The authors feel this is probably the most reasonable approach since it accommodates the unique aspects of rapidly changing industries and products. This method has been used by a few studies, the most recent being the 2009 article published by Shah et al., which used a combination of process LCA data (for electronic components) and EIOLCA data for remaining components (22). The results of this latest study suggest that the use phase of a desktop PC exacts a higher energy and CO2 impact than the manufacturing phase (22). Still, not all hybrid economic input-output LCAs arrive at the same conclusion. As a result of limited data across the supply chain, a few studies have suggested that the embedded carbon in computer products, such as desktop PCs, result in higher energy demand during the manufacture phase than the use phase (8-11). At first glance, differences in the products analyzed and system boundaries may account for the variation seen among studies. However, a closer examination of both the data and methods reveals a high degree of error associated with the use of the “remaining value” component and questionable product price data. In Table 1, three studies which concluded that the energy and CO2 impact from PC manufacture exceeds that from its use were developed by the same author. Results from these studies are presented in Table 4. Although the functional unit is the same across all three studies (desktop computer with 17-in. CRT monitor), the varying results stem from different methods and data used for each analysis. For instance, in the first study, a lack of a VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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standard conversion factor leads to inconsistent results. For details, refer to Supporting Information S2. In the 2003 study, the author arrives at the 240 kg of fossil fuels by summing data from 1995 (for silicon wafers) and 1999 (electronic materials) and other components including CRT and unspecified bulk materials (9). The amount of fossil fuels required for the “manufacture of parts” and “assembly of computer” are not provided. To verify the initial finding of 240 kg, the author includes an approach based on economic input-output analysis. Using an “average” 1998 PC price of $1100, the author arrives also at a final figure of 240 kg of fossil fuels by multiplying the average PC price with electricity (kWh) and fossil fuels (MJ) per dollar of computers produced. According to the author, 0.22 kg of embedded fossil fuels is required per dollar of computers produced. This figure is based on 1997 IO tables and are somewhat fixed since data are updated every five years. Therefore, it is assumed that energy intensity data from IO tables will not change until 2002. Limitations associated with age of IO data have been discussed in the previous section. Meanwhile, PC price will differ depending on the year selected and the data source used. Another variable that leads to dramatically different results by using this particular approach is pricing. For example, if one were to use the average 1998 desktop personal computer price of $1618 cited by IDC Worldwide Quarterly PC Tracker (32), then the estimated amount of fossil fuels used per computer would automatically increase by 45%, from 240 to 350 kg. This increase is not based on environmental data but on price alone since IDC’s $1618 average computer price in 1998 is 45% more than the $1100 price chosen by the original author. The continually decreasing trend in PC prices also illustrates the inability of this method to accurately quantify environmental impact. According to IDC, PC prices decreased by over 9% annually from 1998-2002. Since the IO method assumes a linear relationship between price and fossil fuel use, an analysis using this method would conclude that the total amount of fossil fuels required to manufacture a PC in 2002 would be almost 40% less than in 1998 based on price alone. Again, while it can be assumed that economic and environmental activity are connected, a linear correlation at the product level cannot be assumed. Consequently, it can be argued that product price cannot be used as an exact predictor of a product’s absolute environmental impact. Another issue associated with the use of “average PC price” is the difference between desktop PC and portable (also known as notebook or laptop) PC prices. The amount of electricity and fossil fuels used per computer in this model does not appear to differentiate between desktop and notebook PCs. Therefore, if one were to use the average portable PC price for 1998 from IDC, ($2395), this would result in a more than 50% increase in fossil fuels required (526 kg) due to the price difference alone. For the two studies published in 2004, a different method is used. Total energy is calculated by adding process analysis data with two other components: 1) IO correction factors for semiconductor components, manufacturing equipment and passive components and 2) correction factor to cover the “remaining or residual value” of the product not accounted for by process LCA data or the IO correction factors such as transport, packaging, and other processes (Figure 2). The following sections will analyze these components in order to understand the accuracy and validity of this method. A. Process LCA Data. Although the exact numbers, including totals, differ slightly between the two publications, Williams estimates approximately 3100-3200 MJ are required to manufacture and assemble the principal components of a desktop PC: semiconductors, printed circuit board, cathode ray tube (CRT) monitor, silicon wafers, and bulk materials. 7340
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However, even these values can vary considerably, depending upon what is assumed about the amount of materials in an average unit. Table 2 in the 2004 ES&T paper states that semiconductor manufacturing requires 1.5 kWh of electricity per cm2 of silicon excluding the external, semiconductor die package (11). This number is fairly consistent with estimates used by the semiconductor industry. Table 3 in the same paper then states that 170 kWh is required to manufacture all of the semiconductors in a finished desktop. This implies that an average desktop contains approximately 110 cm2 of semiconductor silicon. In order to validate this assumption, Intel’s internal laboratory measured the amount of silicon in components belonging to a typical desktop PC. The authors were unable to find components manufactured in 2000. Using 2-dimensional X-ray, Intel tested a processor, hard drive, and desktop board manufactured in 2002 and concluded that it contained approximately 7.5 cm2 of silicon. This analysis excluded memory cards, but test data from the 2009 sample suggests silicon from memory cards will not significantly alter the final result. This measured value suggests the energy required to produce all of the semiconductors in a desktop PC would be 62MJ (including both electricity and direct fossil fuels) rather than the 909 MJ quoted in the original studies. The total process LCA energy impact would also change and be approximately 30% lower: 2300 MJ rather than 3200 MJ. The 2004 ES&T article also states that an average of 34 kWh of electricity and 116 MJ of direct fossil fuel are required to manufacture 1 m2 of printed circuit board. The amount of energy attributed to the printed circuit board in an average desktop is given as 7.71 kWh and 26.7 MJ, suggesting a total of 0.23 m2 of circuit board per unit. Intel’s analysis of the desktop board mentioned above showed that it has a total area of 0.05 m2. Again, if this value were used, it would significantly change the resulting total energy number for the complete desktop unit. In this case, the energy attributed to the circuit board would drop from the 54.5 MJ quoted in the original study to 11.8 MJ. The assumptions regarding material inputs contained within this total are shown in Table 5. Intel also tested a high-end desktop board manufactured in 2009 for comparison. Measurements show that the printed circuit board’s area of 0.07 m2 is still far less than the 0.23-0.27 m2 estimated in the previous studies (10, 11). The 2009 analysis included memory but did not include the hard drive. Based on the testing of the 2002 samples, the total amount of silicon was distributed as follows: hard drive (23%), processor, (17%), printed circuit board (60%). If the same proportions were assumed for the 2009 desktop board, the authors estimate that the total amount of silicon in a typical 2009 desktop system is approximately 12 cm2 including the hard drive. Again, this amount is decidedly lower than the 110 cm2 cited in previous studies, which appear to be significant overestimates. While it is possible that the earlier studies sought to account for a certain portion of inputs that are not included in the finished product (yield losses in silicon and printed circuit board manufacture), the 16-fold difference would suggest that yield would be only 7% and losses would be approximately 93% based on the measurements of the components manufactured in 2002. Yield losses are a legitimate factor to include but would not be large enough to explain the differences seen in Table 5. Notably, the amount of energy (kWh/cm2) needed to produce wafers or semiconductor devices cited by Williams appears to be similar to other studies. Indeed, the disparities, error, and/or uncertainty are introduced when these process data are combined with production data at the industrial or economic level in order to estimate the total amount of energy associated per unit.
TABLE 5. Assumptions Used in Previous Studies Compared with Data Collected in 2009 assumptions used in 2004 studies (10, 11)
data proposed by authors based on a 2002 system
data proposed by authors based on a 2009 system
semiconductors (cm2 of silicon per desktop)
110 cm2 (estimated)
7.5 cm2 (measured data from processor, desktop board, and hard drive)
printed circuit board (m2 of printed circuit board per desktop) energy to produce all semiconductor devices per desktop
0.23-0.27 m2 (estimated)
0.05 m2 (measured)
7 cm2 (measured data from desktop board incl. memory) 12 cm2 (estimated to include hard drive) 0.07 m2 (measured)
909 MJ
energy to produce printed circuit board per desktop
54.5 MJ
62 MJ (estimated by combining 7.5 cm2 with method used in 2004 studies 11.8 MJ (estimated by combining measured data with method used in 2004 studies)
99 MJ (estimated by combining 12 cm2 with method used in 2004 studies) 16.6 MJ (estimated by combining measured data with method used in 2004 studies)
The disparities seen in Table 5 suggest variation is caused by the incorporation of industry-level, global data such as global wafer production data and global share of desktop computer sales in order to estimate energy per unit computer. In this case, a more accurate method may also be a simpler one that relies solely on unit data, as shown in Figure 4. B. IO Correction Factors. In most hybrid LCA analyses, IO data are used in areas for which process LCA data are not available. For the two studies published in 2004, the IO approach is used to estimate the energy requirement per desktop for 1) specialty chemicals/materials, 2) semiconductor manufacturing equipment, and 3) passive electronic components. The unit energy requirements for each of these three components are the products of a) estimated value ($) of each contained within each desktop (producer price of the product) and b) supply chain energy intensity (MJ/$) (11). Specialized Chemicals and Materials. Economic correction factors are likely to overestimate the energy impact from the manufacture of specialty chemicals and materials used in electronics manufacturing. The materials and chemicals used can be very expensive and therefore, in an I/O analysis, will be attributed a high environmental (and specifically energy) impact value. Previous studies have suggested that semiconductor chemicals require additional energy to achieve higher purity resulting in not only higher cost but higher environmental impact (10, 11, 19, 36). However, it cannot be assumed that energy is the key determinant of cost since they are not linearly related.
To test this assumption, the authors of this paper took figures gathered from their own recently published study and compared them to the cost of those materials (21). This study utilized commercially available software to estimate the CO2e impact associated with the production of those materials. This was a “cradle-to-gate” assessment covering all aspects of material production from raw material extraction through material manufacturing. Conversions of energy value to CO2e were based on U.S. average values and include conversion losses. Note that other electricity data discussed in this article are limited to direct electricity use and do not incorporate conversion losses. Specific materials are not identified in order to protect purchasing contracts, but all are chemicals and gases used in semiconductor manufacturing and support. The results are shown in Figure 5 below. A correlation coefficient (r) of 0.07 suggests a lack of a linear relationship between the two variables. The r-squared value is 0.005 for the materials in the chart which suggests that less than 1% of the variance associated with CO2 impact can be predicted by cost. Removing the outlier (the chemical whose cost is ∼$14/kg), the correlation coefficient and r-squared value increases slightly to 0.36 and 0.13, respectively. Both of these values indicate that chemical cost and CO2 impact are not significantly correlated and that factors other than energy intensity of manufacturing are more important in determining material cost. This would be consistent with a previous study which found that using product prices as a proxy for manufacturing energy intensity is not “plausible” since pricing is determined by other factors (36). One such factor is Research and Development (R&D). The PC industry and its supply chain, including suppliers of electronic chemicals, peripheral equipment, and semiconductors, invest heavily in the R&D of new products. According
FIGURE 3. Equation used by Williams (10, 11) to derive “process analysis” data.
FIGURE 4. Alternative equation to derive “process analysis” data which requires less data and yields more accurate results.
FIGURE 5. Chemical cost and CO2 impact. VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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TABLE 6. R&D Intensity of Computer and Electronic Products Industriesa R&D expenses/revenue (%)
all industries manufacturing industries chemicals computer and electronic products a
NAICS code
2003
2004
2005
2006
2007
21-23, 31-33, 42, 44-81 31-33 325 334
3.2 3.1 5.6 9.3
3.4 3.4 6.6 8
3.3 3.6 6.9 9
3.4 3.6 7.5 9.2
3.5 3.7 7.9 8.4
Source: National Science Board (37).
TABLE 7. Original Data from Previous Study Published in ES&T (11) global sector per value accounted for in revenue ($B) desktop ($) share process analysis ($) semiconductor circuit boards CRT monitor silicon wafer bulk materials assembly total
204 42.7 19.5 7.5 n/a 248 521.7
634 57 180 23 29 1700 2623
61% 47% 38% 53% 35% 35%
387 27 68 12 10 595 1100
to the National Science Board’s Science and Engineering Indicators 2010, six industries, including the chemicals and computer and electronic products industries, accounted for approximately 78% of company-funded business R&D (37). Based on 2003-2007 data cited by NSB, the computer and electronic products industry, along with the software and computer-related services industries, comprised 34% of industrial R&D. In terms of R&D intensity, which can be defined as the ratio of a company’s R&D expenditures to its revenue, the average R&D intensity across all industries and also manufacturing industries ranges from 3.1% to 3.7%. The chemical and computer and electronic product industries are particularly intensive compared to other manufacturing industries with R&D intensities ranging from 5.6% to 9.3%, Table 6. While not intended to be an exhaustive comparison, the data do further suggest that R&D costs for computer equipment and semiconductor manufacturing industries tend to be higher and thus figure significantly in final product cost. Although energy intensity of different industries is not something that is widely tracked or studied, there are data that can be used to test the assumption that the manufacture of Personal Computers and input materials is highly energy intensive. The Carbon Disclosure Project (CDP) collects information on energy and CO2 impacts from large companies around the world. In 2008, the CDP collected CO2 emissions data from 383 of the world’s largest companies and used this information to calculate CO2 intensity (reported as CO2 emissions per $ of revenue) for reporting companies. Although the authors do not believe 2008 CDP data can be used to quantitatively rank the energy intensity of specific companies due to variable reporting methods and lack of Scope 3 emissions data, the data analyzed from 2008 and, specifically Figure 24 in the 2008 CDP Global Report, do suggest that a) the Technology, Media, and Telecoms industry and b) industries that produce high-priced products are not among the most energy intensive as some LCA studies have suggested (38). While it may be assumed that R&D intensity is (inversely) correlated to carbon intensity, the authors do not feel such a conclusion can be drawn without further analysis. Any linkage between R&D intensity to carbon intensity will be dependent upon the given industry and nature of the R&D. However, the data shown in Table 6 suggest the R&D expenditures of computer and semiconductor device manu7342
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facturing industries are comprised mainly of intangible costs (salaries, wages, and other related costs of personnel engaged in R&D activities; and other indirect costs, except for general and administrative costs) as opposed to costs associated with R&D materials, equipment, and facilities. The complex determinants of product price, including R&D expenditures, render product price an unreliable predictor of energy intensity. C. Remaining Value. A more significant limitation in the method used in previous studies lies in the remaining value component (10, 11). Remaining value is calculated by taking the total estimated value of the components covered in a) process LCA and b) IO and subtracting this amount from an average PC price for a given year. The main factors in this calculation are as follows: a) average value ($) per desktop of certain components (semiconductor, circuit boards, CRT monitor, silicon wafer, bulk materials) and assembly, b) average PC price for a given year, and c) relative value share (%). The difference in total energy seen between the previously referenced 2004 studies from Table 4 (7400 MJ and 6400 MJ) is due to the “remaining value” component, but a full explanation is not provided. This difference is noteworthy since the functional unit, process data, and IO additive factors appear to be the same between the two studies. Both use an average PC price ($1700), from which the estimated value represented by the process data ($1100) and additive IO factors ($260) were subtracted, leaving a remaining value of $440. While the ES&T article includes an additional line item for “packaging and documentation”, it does not account for the nearly 1000 MJ difference between the two papers. The amount attributed to “other processes” in the first paper is 2300 MJ compared to 1192 MJ shown on the latter paper. Yet, despite the identical remaining value, the total energy (MJ) differs between the two studies suggesting the remaining value component is a key source of inaccuracy and should not be used for hybrid life cycle assessments. Indeed, a previous life cycle energy assessment which included a “residual” or “remaining value” component stated that the accuracy of such a method had yet to be confirmed and that “the highest inaccuracy will probably be caused by one part: the energy requirement of the production of residual goods” (31). The following section will discuss additional limitations that render this approach unreliable and unsuitable for ICT products including PCs. Average Value ($) Per Desktop. According to Williams, the first step in calculating remaining value is to estimate the average value ($) per desktop for those components that have process-analysis data (11). The authors were unable to fully replicate the results using the method described in the original study as not all of the underlying data were available. Process analysis data are obtained for semiconductor, circuit boards, CRT, silicon wafer, bulk materials, and assembly. Original data are shown below. A few issues arise. First, the total value ($) per desktop given for semiconductors, circuit boards, CRT monitor, silicon wafer, bulk materials, and assembly is $2623, exceeding the total average price of
FIGURE 6. Relative Pentium III price change from Q1-Q4 2000 (34).
$1700. Such a scenario in which the cost to manufacture and assemble a product exceeds its selling price does not seem plausible. For semiconductors, the estimated $634 per desktop is based on a) 2000 global sector revenue, b) share of semiconductors for desktop PC, and c) number of desktop PCs produced. Since the method to estimate $ per desktop is based on annual revenue and production data, it appears to be relatively fixed for a given year and thus may not accommodate variations in prices (within a given year and across a period of years) effectively. For example, the focus of the original study was a typical desktop system sold in 2000 with a Pentium III processor. Data obtained from a published source indicate Pentium III prices decreased substantially between Q1 and Q4 of 2000 which is not captured in this model (34). Using Q1′2000 average prices for highest, medium, and lowest-priced Pentium III products as a baseline, Figure 6 shows that the average price of Pentium III products dropped 47%-65% in 2000. As previously shown in Figure 1, models which rely on fixed data cannot accommodate industries which undergo rapid and continuous changes in price, manufacturing processes, and production efficiencies. The following section will focus on PC price specifically because of the crucial role it plays in the validity and consistency of this method. Average PC Price. In both of the analyses published in 2004, an average 2000 PC price of $1700 was used with slight variations: in ref 10 $1700 is described as the “average global price of a desktop system in 2000”, whereas in ref 11 $1700 is described as “the average global producer price of a desktop system in 2000” (11). EIO experts have proposed that, while consumer price and producer price may be equal for services and utilities, this is not commonly the case for manufactured goods where consumer prices are higher than producer prices through the addition of cost margins (42). As Hendrickson et al., have noted, the producer price should be used in EIOLCA rather than consumer price (42). Yet, producer price data may be more difficult to obtain so it is understandable for researchers to use average consumer price. In the case of the two previously published studies by Williams, the use of $1700 as either a producer or consumer price for a ‘typical desktop system’ appears to be inaccurate when compared with industry-accepted data. Using $1700 as the average consumer price for a desktop system in 2000 is counter to the generally accepted trend of decreasing PC prices (Figure 7). In fact, this average price is over 50% higher than the $1100 average 1998 PC price used in the first study and at
FIGURE 7. Comparison of PC price trends 1998-2002. least 30% higher than publicly reported average PC prices which range from $1000 to $1300 for a similarly equipped PC (including monitor and peripherals) (32, 33). Figure 8 shows the impact a different PC price will have on the remaining value’s contribution to the total energy demand calculation used in the 2004 studies. Compared to the 6400-7500 MJ estimated by the previous study, the authors arrive at a total of 4500-4600 MJ. The variation is due specifically to the remaining value component. To ensure comparability, the authors adjusted the value of total computer production and number of desktop computers using year 2000 data from IDC. The amount accounted for by process analysis remained similar at $1100. However, the remaining value falls by approximately 90% to $40. As such the contribution to total energy decreases proportionately from 2100-3200 MJ to 200-300 MJ. Using $1700 as the average producer price for a desktop system in 2000 faces the same complications. As described in the previous paragraph, $1700 as a consumer price is higher than widely accepted sources. As a producer price, the accuracy of $1700 is even more questionable. According to Mercury Research, the average producer price in 2000 for a desktop computer sold at a consumer price of $1750 was 30% less. In other words, the average cost to build a desktop computer sold for $1750 was approximately $1200. In both cases, as a consumer or producer price, $1700 significantly overestimates the price of a typical 2000 desktop personal computer. This overestimation leads to results that are highly sensitive and prone to error. To minimize the potential error, it is recommended that future assessments use price data from widely accepted sources (e.g., IDC, Gartner, Consumer Electronics Association, etc.) to ensure accurate figures based on valid and transparent methods are used. 3. Implications. By reviewing several previously published studies, the authors sought to better understand hybrid LCA methods used for the desktop personal computer. The authors believe that LCA analyses relying fully or partially on pricing data and remaining value have a tendency to overstate the energy impact of the manufacturing and premanufacturing phases for both semiconductors and computer systems. The authors have also determined through testing and analysis that the use of remaining value as used in previous studies (10, 11) leads to significant error and does not fit ICT products such as desktop personal computers well for the following reasons: VOL. 44, NO. 19, 2010 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 8. Impact of PC price on remaining value’s contribution to total energy. • The rapid development of new technology and product changes. • High price volatility and specifically product prices that tend to decrease significantly over time. • Poor correlation between cost and energy and environmental (e.g., CO2) impacts, both for finished products and for input materials. Research and development and intellectual property costs are likely far more significant cost drivers for these materials. While the intent of remaining value is understood to account for the entire supply chain, the current application of remaining value does not yield accurate results due to possible double-counting and lack of robust and industryaccepted pricing data. Indeed, the pricing data used in previous studies (10, 11) appear markedly inconsistent with widely accepted and referenced sources such as IDC. This inconsistency amplifies the limitations associated with remaining value and yields questionable results. Input-output analysis has been described as being less accurate and specific than process-analysis LCA, and researchers have suggested minimizing the use of this method, particularly for “single products or processes” (27, 28, 31). Process-sum life cycle assessments of course have limitations as well. One of the chief limitations is the portion of the impact that is lost when boundaries are drawn defining the parts of the supply chain and supporting operations included in the analysis. While the authors acknowledge that EIOLCA methods are intended to reduce the time, expense, and resources required to conduct LCAs for ICT products such as PCs, process LCA still appears to be the more accurate route to estimating a product’s environmental impact; in this case, the total energy required to manufacture a product. Therefore, it is essential for researchers to carefully consider how these two methods can be combined to yield accurate and valid results. The wide range in methods, data, and conclusions from previous hybrid LCA studies hinder the 7344
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method’s acceptance, practice, and applicability. As with any method that has yet to overcome significant sources of uncertainty, we recommend these methods be used to identify trends or areas for future research rather than used to quantitatively predict a product or even an industry’s environmental impact (42). Hybrid LCA may work best if product-specific data are obtained using the process LCA method and augmented by IO data for those aspects that go beyond the manufacture, use, and disposal stages such as distribution and transportation. Studies such as Shah et al. provide one possible method in which product process LCA data are combined with IO data for secondary aspects. More importantly, the authors hope that this analysis shows that hybrid LCA methods such as the one used in previous studies (9-11) based on remaining value are not appropriate given the high sensitivity to price data. The various methods currently used under the umbrella of “hybrid LCA” illustrate the continuing need for LCA practitioners and ICT researchers to collaborate, compile, and define standardized data and methods. Suggestions for Future Research. When considering further refinement of LCA models used to evaluate the environmental impacts of personal computers, an interesting set of data is emerging that should be further studied and integrated into future LCA studies. Namely, recent studies have suggested that the use of ICT equipment results in a net energy savings due to efficiencies associated with its use and integration. The magnitude of this impact may be significant. In what is termed the “ICT energy paradox”, one study concluded that for every extra kilowatt-hour of electricity used by ICT equipment, the U.S. economy increased its overall energy savings by a factor of about 10 (39). Two other studies suggest that the opportunity for energy savings from IT equipment in Europe could be 5-7 times the total energy consumption of all ICT products in 2020 (39, 41). Naturally these studies also rely on complex sets of assumptions, each of which have
their own limitations and potential for error. The difficulty in predicting future usage patterns, consumer adoption, product designs, and energy requirements render estimations of ‘second-order’ ICT impacts equally challenging. As previous studies have proposed, rebound effects may arise that could offset the environmental benefits associated with increased optimization and dematerialization associated with continued technological advances (43). While this paper did not attempt to quantify the errors in those studies, the authors believe such work suggests that focusing only on the life cycle impacts associated with the manufacturing and use of electronics may not paint a complete picture. The environmental benefits associated with the product use should also be taken into consideration in order to understand complete life cycle impacts. As advanced electronics become more widely embedded in the economy (via smart grid, smart buildings, smart transportation systems, etc.) this area is worthy of additional study.
Acknowledgments The authors wish to thank Dick Casali, Roland Chin, John Harland, Constantin Hermann, Suzanne Hopkins, Dean McCarron, Grant Metzgar, Ted Reichelt, Lisa Sammon, Mike Shirer, Steve Sullivan, and Tri Than.
Supporting Information Available Definitions of various types of life cycle assessments and a review of calculations used in a previously published study and types of life cycle assessments. This material is available free of charge via the Internet at http://pubs.acs.org.
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