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Input-Output Based Life Cycle Analysis for New Economy Models

Input-Output Based Life Cycle Analysis for New Economy Models 

H. Scott Matthews*

Department of Civil and Environmental Engineering

Department of Engineering and Public Policy

Carnegie Mellon University

Porter Hall 119

Pittsburgh, PA 15213 USA


Chris T. Hendrickson

Head, Department of Civil and Environmental Engineering

Carnegie Mellon University

Porter Hall 119

Pittsburgh, PA 15213 USA




Using the 1992 U.S. Benchmark and 1997 Annual Input-Output tables, we have created a freely available Internet tool to facilitate I-O analysis at www.eiolca.net.  Beyond economic transaction data, the site has sector-level data on fuel and electricity use, conventional air pollutant and greenhouse gas emissions, toxic and hazardous waste emissions, employment, worker safety and fatalities, ores and fertilizers.  Our primary purpose for providing this information is to facilitate Life Cycle Assessment of products and processes, an environmental analysis tool to track all resource inputs and environmental outputs from extraction of raw materials through product disposition. 

Extensions of the model allow users to enter multiple sectoral demands, graph results, and show side-by-side comparisons.  The architecture of the model is open, and input-output data from other countries can be accommodated (and is being solicited) via the same web-based portal. German and Japanese I-O data will be added in 2002. 

We demonstrate this model by comparing the energy use and greenhouse gas emissions for buying books from online vendors versus traditional retail bookstores in the United States.  In addition, we compare our results with results from a non-input-output based estimate for book purchasing in Japan.


1.  Introduction to EIO-LCA Model and Data Sources


The Economic Input-Output Life Cycle Assessment (EIO-LCA) model was originally developed in 1995 to describe connections between economic activity, energy and resource use, and environmental emissions using the 1987 Benchmark Input-Output Table developed by the U.S. Department of Commerce Bureau of Economic Analysis [Lave 1995].  The EIO-LCA model was originally developed as a proprietary Microsoft Windows application.  However, a grant from the U.S. National Science Foundation in 1998 allowed development of a free, Internet-based version of the model and an update to the 1992 Benchmark I-O table [Lawson 1997, Hendrickson 1998].  Since 2000, nearly 100,000 analytical comparisons have been made using this tool, available at http://www.eiolca.net/.  Amongst the many features available are direct and total supply chain estimations from a given input change in multiple sectors of demand, analysis of aggregate and disaggregate models, corresponding estimates of non-economic effects, and bar graphs of individual and total sector contributions. 

The EIO-LCA model was created in a modular way so that new input-output data (as well as corresponding non-economic vectors) could be easily added and updated.  Benchmark I-O tables for the US economy are available every five years (e.g. 1987 and 1992) and are released 4-5 years subsequently.  In 2001, we updated to the 1997 Annual Input-Output table from the U.S. Department of Commerce [Kuhbach 2001].  In 2002, we will make available the German Statistical Office 1993 German input-output table as well as information on conventional pollutant and greenhouse gas emissions for a roughly 60-sector model.  In late 2002, we will update to the 1997 U.S. Benchmark and present a similar model for the Japanese economy.   

Our motivation for providing such comprehensive supplemental non-economic data support in conjunction with the I-O tables is to facilitate environmentally conscious decision making.  One approach to support environmentally conscious decision-making is life cycle assessment (LCA). In a U.S. Environmental Protection Agency (EPA) document, Vigon [1993] defines life cycle assessment as “A concept and a methodology to evaluate the environmental effects of a product or activity holistically, by analyzing the entire life cycle of a particular product, process, or activity. The life cycle concept has often been referred to as “cradle to grave” assessment. A life cycle view of a product is intended to yield environmental improvement by revealing the complete environmental picture of a product, rather than just the emissions generated in the usual course of production by the manufacturer. LCA is useful beyond the scope of a manufacturer as well. Service providers, government agencies, and other interested parties can use these methods to consider the total impact of their global business activities.  

The Society for Environmental Toxicology and Chemistry (SETAC) life-cycle assessment technical framework workshop report published in January 1991 summarized the status of the field at that time and was one of the initial documents that outlined a basis for life cycle studies [SETAC 1991]. The United States Environmental Protection Agency (EPA) [Vigon 1993, Curran 1996] accepted and built on the SETAC framework. The research by EPA and SETAC led to a four-part approach to LCA that is accepted today:  scooping and definition, inventory, impact assessment, improvement analysis.   The major obstacles in performing life cycle assessments are in dealing with boundaries and circularities within the system of study and in collecting the necessary data.  In defining the "boundary" of the analysis, e.g., in the LCA of a paper cup by the manufacturer, the practitioner may decide to consider only the inventory of effects arising from the 10 highest cost items in the production process to save time and effort. The boundary assumption is an important one, as it draws the line around what will be excluded from consideration in the inventory, and inevitably, from the overall assessment. The necessary data collection and interpretation is contingent on a proper understanding of where each stage of a life cycle begins and ends. Any effects that lie outside of the boundary are ignored. This boundary assumption can potentially lead to significant under-estimation of the inventory of effects of a product across its life cycle.   Circularities arise when boundaries are drawn broadly, and ‘feedback loops’ exist, e.g. when considering the LCA of steel there is some trucking of materials to factories  needed, and the manufacture of trucks requires steel. 

Quantifying the inputs and outputs (materials and energy use, and environmental discharges) associated with each stage of the life cycle is an exhaustive task. This is typically accomplished by either initiating new research to estimate the inventory data, or consulting existing databases of inventory information. Data collection is driven by the study’s goal. For example, existing estimates on the environmental impact of electricity generation can be used, but such estimates need to be relevant and specific to the particular application to be of use in such a setting. To yield accurate results, the inventory of electricity effects for a particular processing plant need to be relevant to the local mix of electricity purchased. Thus, estimates need to be available that reflect the use of renewable or non-renewable resources used to make the plant's power. Getting such data could be difficult, as it will rely on fuel and technology assumptions (e.g. data on a power plant burning low-sulfur coal or using flue-gas desulfurization for control of air emissions). In the end, the success or failure of any LCA will depend greatly on the boundary assumptions, data quality, and the level of economic resources available. In short, the data requirements for even a small system can be tremendous. 

Life Cycle Impact Assessment (LCIA) further extends the analysis and interprets the results of the inventory in order to assess the impacts of the product or project on human health and the environment. EPA [2000] reports “Impact indicators are used to measure the potential for the impact to occur rather than directly quantifying actual impacts. This approach works well to simplify the LCA process making it a more useful tool. A variety of environmental impact indicators and associated indicators have been developed and more continue to be used as LCA method evolves. The categories for indicators range from a global level, such as contribution to global warming and ozone depletion, to local impacts, such as photochemical smog formation. As an example, a recent study conducted for the US EPA defines eight impact categories and indicators for: global climate change, stratospheric ozone depletion, acidification, photochemical smog, eutrophication, human toxicity, ecological toxicity, and resource depletion.  

Finally, the improvement analysis is a systematic evaluation of the needs and opportunities to reduce the environmental impacts, energy use, and materials use during the lifecycle. This analysis may include both quantitative and qualitative measures of improvements. Introductory material on life cycle analysis is further documented in Barnthouse [1997], Curran [1996], Graedel [1995], and Vigon [1993].  

As stated above, a life cycle includes all the steps, from extracting the resources to product disposal. Each of these life cycle steps has impacts on the environment. For example, raw materials extraction results in depletion of nonrenewable resources like petroleum and ores. In the case of ores, mining machinery requires large quantities of energy, generally from burning fossil fuels that release carbon monoxide, nitrogen oxides, and particulate matter into the air. Manufacturing and disposal of a product also require energy and result in discharges. In short, every step of a product's life cycle has both inputs from and discharge to the environment. Over the life cycle, the sum of these inputs and releases can be substantial.


Many SETAC-based LCA software tools exist to aid decision-makers in performing life cycle inventories. Most consist of a graphical user interface front-end to a database of existing product or process-specific inventory data. The inventory data may be proprietary, from public data sources, or both. If boundaries are appropriate and care is taken in selecting data sources, meaningful and relevant results are possible. However the accuracy costs time and money. It is not uncommon for detailed LCA studies to require hundreds of thousands of dollars and 6 months to complete. If time is a primary driver, results may not be available until the next production cycle, which is often too late to make improvements. 

The concern over the cost and time required for LCA has resulted in researchers investigating methods to simply the analysis while still retaining the information needed to satisfy the study goals. One concept that has received attention is Streamlining LCA. EPA 2000 describes this concept as follows; “A continuing concern over the cost and time required for LCA encouraged some practitioners to investigate the possibility of “streamlining” or simplifying LCA to make it more feasible and more immediately relevant without losing the key features of a life-cycle approach. When the concept of streamlining was first introduced, many LCA practitioners were skeptical, stating that LCA could not be streamlined. Over time, however, there has been growing recognition that “full-scale” LCA and streamlined LCA are not two separate approaches but are, instead, points on a continuum. As a result, streamlining an LCA becomes part of the scope and goal definition process. The key is to ensure that the streamlining steps are consistent with the study goals and anticipated uses, and that the information produced will meet the users’ needs. From this perspective, the scope and goal definition process involves determination of what needs to be included in the study to support the anticipated application and decision”. The concept of Streamlined LCA is developed in Graedel [1998];  Bennett [2000] presents an example using the approach. 

Despite these advances, this LCA approach still requires setting tight boundaries around the problem to make it tractable. As we show below, the parts of the supply chain that are outside the boundaries are generally important, leading to significant changes in the use of resources and environmental discharges. Thus, while this LCA approach has become easier and cheaper to apply, it still has the inherent difficulty of excluding a significant part of the life cycle. 

The limitations of traditional SETAC LCA methods are largely addressed in an input-output model based LCA system.  For example, production of goods and services can be completely traced through the supply chain with EIO-LCA (i.e. the boundary is all production in the economy).  Circularity is overcome by use of the Leontief equation.  By considering additional ‘use-phase’ purchases (e.g. electricity use), the entire life cycle of implications can be considered.  For example, given appropriate information on currency differences, a user could see the implications of producing $1 million US dollars’ worth of steel in several countries at once.  When coupled with environmental or energy data, users could quickly see the energy efficiency differences present in each nation. 

Below we detail more of the available analytical tools available in the EIO-LCA model, discuss data sources and availability, solicit additional help in enlarging the data sets, and show an example of EIO-LCA analysis for online versus traditional book publishing in the new economy.


Model and Data Sources


As noted above, the EIO-LCA model is organized in a modular way to allow any input-output table to be used as a basis for a Leontief type modeling exercise.  It has been designed to be extended as data is updated and other country IO data becomes available.  Currently the EIO-LCA model has the following input-output data sources: 

  • 1992 U.S. Benchmark – Large 485 sector and smaller 97 sector models
  • 1997 U.S. Annual – Large 485 sector and smaller 97 sector models
  • 1993 German – 60 sectors

The U.S. models are supplemented by publicly available sector-level data on the following non-economic effects (with government sources noted): 

  • Employment – from U.S Department of Commerce (DOC) Economic Census 
  • Energy: Electricity and Fuel Use – from Department of Commerce fuel purchase data and Fuel and Electric Industry reports, Department of Energy Manufacturing Electricity Consumption Survey, and the Transportation Energy Data Book.
  • Ore and fertilizer use – from IO commodity work files supplied by DOC
  • Conventional Pollutant emissions (e.g. sulfur dioxide, carbon monoxide, etc.) – from U.S Environmental Protection Agency AIRS data
  • Greenhouse Gas emissions – from fuel use data above, combined with EPA AP-42 emissions factors, IPCC global warming potential weighting factors.
  • Toxic Chemical Releases – from US EPA Toxics Release Inventory
  • RCRA Hazardous Waste – from EPA
  • Water Use/Recycling – from Department of Commerce
  • Worker Health and Safety – from OSHA/Department of Labor

As consistent with I-O modeling, any vector of effects that is available for all sectors on a per-unit-of-output basis can be included in EIO-LCA.  Further, any additional national IO tables can be easily added.


2.  Sample Application of EIO-LCA to New Economy


It is tempting to assume that the sale of products on the Internet is beneficial to the environment. For example, emissions from vehicles driven to shopping malls can be avoided, retail space can be reduced, and inventories and waste can be reduced. However, a product ordered online may be shipped partially by airfreight across the country and require local truck delivery. Also, the product is likely to be packaged individually, and the packaging may not be reused. For urban dwellers relying on public transportation, delivery by courier service probably implies increased fossil fuel use. Residential energy consumption will increase somewhat with additional time spent at home shopping on-line. The adverse impacts on the environment can be significant, and the net effect of different logistics systems is not obvious. 

Books are regularly purchased online as well as in retail stores. This work compares environmental and economic performance of traditional retailing and e-commerce logistic networks for the case of books.  Traditional retailing involves a retail outlet to which books are shipped from the publisher through distributors and warehouses. The customer then purchases the book at a retail store and brings it home. The e-commerce model ships the book from the publisher to a warehouse then to a courier's regional hub where it is sent by delivery truck to the customer. 

The high number of remainders (unsold books) suggests an additional factor favoring online retailing. After sales have peaked, these remainders are either discarded, recycled or sold to a discount bookstore. E-commerce allows for lower inventories and fewer remainders at the sales end of the supply chain (since there is only one inventory point), thus possibly reaping environmental benefits due to avoided warehousing and paper production. Romm and collaborators suggest that the reduced building space requirements of the online retailing imply lower energy consumption [Romm 1999]. Matthews [2000] outlined factors contributing to environmental performance.  

This work initiates the task of quantative systems analysis comparing the two logistics systems. We present two life cycle assessments of online versus traditional retailing, one for the case of the US and a subsequent one for Japan. In terms of methodology, the former uses an extended version of economic input-output Life Cycle Assessment (EIO-LCA) [CMU GDI 2002], which includes total supply chain effects, while the latter is based on a traditional LCA. Both studies consider the energy consumed in distribution, packaging and personal transport, but beyond these basic factors the focus issues are different. The US study has a larger system boundary, and highlights switching of truck-rail-air modes and inventory reductions associated with online retailing, while the Japan work focuses on the effect of population density, mode of consumer transport, and changes in residental energy consumption. 

Case Study of Energy Requirements from Book Retailing in the United States1 

The traditional method of retail, where books are sold at retail stores, can be modeled as a series of transport links among facilities. The books are printed, transported to a national warehouse, and then shipped again to a regional warehouse. From the regional warehouse, the books are transported to a retail store, where a customer buys a book and takes it home. In addition there is a return link for unsold copies as roughly 35% of best sellers are unsold [Publishers Weekly 1997]. We assume all transportation is carried out by truck, and the distance between all destinations (e.g. warehouses and stores) is separated into segments of 805 km (500 miles). The average consumer lives 16 km (10 miles) away from a bookstore [Brynjolfsson 2000] but consumers tend to buy more than one item at a bookstore (or as part of a shopping trip); thus, only a round-trip distance of 5 miles (8.3 km) was used for the round-trip to the bookstore.  This model builds upon Matthews [2001]. 

We assume that the 35% remainder rate for books in traditional retail inherently causes the production of 35% more books than sold (or a total of 386,000 books). All of these books are transported in boxes of 10 to bookstores. Assuming that each box is 51 x 41 x 41 cm (20 x 16 x 16 inches) and weighs 910 grams (2 pounds), the cost of each box is $1.33 [ULINE 2001]. 

The environmental effects of automotive trips made by consumers to bookstores to purchase books must also be taken into account. Assuming the fuel economy of a passenger car to be 9.6 km/l (22.5 mpg), and the fuel economy of a light truck to be 6.5 km/liter (15.3 mpg) [US EPA 1997], we can calculate that the energy required per mile for a passenger car is 3.6 MJ/km (5.8MJ/mi) and for a light truck is 5.3 MJ/km (8.6 MJ/mi). Thus, the 5 mile (8.3 km) round trips by passenger vehicle would require 29 and 43 MJ, respectively.  Assuming the fleet is 65% cars [NHTSA 1998] and 286,000 trips are made, 9.7 TJ of energy would be required for all trips. Energy is also required to produce the fuel consumed in these trips, as seen below. 

Returns of unsold books from retailers in the traditional model are an important issue. Shipping of returns involves an additional truck leg, which we again assume to be 805 km (500 miles). We ignore returns from customers after purchases; we assume they would involve similar personal trips for both traditional and e-commerce retailing. 

In the e-commerce method of selling a book, we assume that the books are bulk shipped 805 km (500 miles) from the printer to the company’s major distribution warehouse via truck. We also assume that this warehouse is located near or at an air hub of a major logistics carrier (e.g. UPS or FedEx) so transfer from warehouse to the carrier is negligible. When an order is received, the books are then air freighted or sent by truck to a regional courier center (again assuming a distance of 805 km) from which the books are delivered by local courier truck to the customer’s residence.  The air shipping scenario represents a 'worst case' scenario for analysis. 

The packaging used in e-commerce tends to be corrugated cardboard boxes. Using an actual amazon.com shipping box for a single book, we assume a box size of 30 x 23 x 11 cm (12 x 9 x 4.5 inches), a weight of 317 g (0.7 pounds), the cost of each box to be $0.41 [ULINE 2001] and that the books are packaged individually, we can calculate the cost in individually packaging the total shipment of $1 million worth of books as $117,000. We assume no remainders or returns in this model. However, the cost of the bulk packaging of 286,000 books also needs to be included, $38,000, for a total of $155,000.


Comparative Costs and Energy Use of Traditional and E-commerce Logistics


Selling $1 million of books in the traditional model with remainders requires 386,000 to be produced and shipped given the 35% remainder rate. The total weight of shipments in the traditional model is 455 Mg (501 short tons)– including 420 Mg (463 short tons) of books and 34.5 Mg (38 short tons) of bulk packaging. A base production of $1 million of bestseller books in the e-commerce (no remainders) model requires only 286,000 books to be shipped. The e-commerce model ships a total of 338 Mg (371 short tons) in bulk (including 343 short tons of books and 29 short tons of packaging) and a total of 403 Mg (443 tons) individually. A comparison of these costs is shown in Table 1. We present estimates for two traditional models - with and without remainders. We use the 35% remainder rate to scale up costs where appropriate.


Item Traditional Retailing E-Commerce by Air Retailing


W/ 35% Return    
  Calculation Notes Cost




Calculation Notes Cost


Packaging $1.33 x 286,000 books/10 books per box 38 51* ($1.33 x 286,000 books/ 10 books/box) + ($0.41 x 286,000 books) 155
Bulk Truck Shipments to Warehouse 3 trips*805 km/ trip (500 mi) of 338 metric tons (371 short tons) at $0.18/mt-km ($0.26/ ton-mile) (US DOT 1999) 144 195* Only one 805 km (500 mile) shipment of 338 metric tons (371 short tons) 48
Air Freight None 0 0 One 805 km (500 miles) trip - 403 tons (443 short tons) at $0.55/mt-km ($0.80/ ton-mile) (US DOT 1999) 177

Delivery/ Pickup

8 km (5 miles) at $0.21/ km ($0.33/mile) for 286,000 pickups. 472 472 Local delivery charge of $1.50 for 286,000 books 440
Retailing Overhead Cost 12% revenue with average $15/book (Meeker 1997) for 286,000 books 515 695* 4% of revenue with average $15/book (Meeker 1997) for 286,000 books 172
Return Shipping from Retailer 805 km (500 mi) of returns (100,000 books *10 books/box * $0.26/mi) 0 17    
Return Production Cost $3.5 of 100,000 books 0 350    
Total without private auto 697 1,308   992
Total with private auto 1,169 1,780   992

Table 1 
Estimated Costs of Logistics and Returns for Traditional versus E-Commerce Book Retailing 

With a zero return rate, the traditional system has a slightly higher overall cost than e-commerce but can provide immediate service to customers. But generally, a certain proportion of the books published will remain unsold, and will be either returned to the publisher to be recycled or sold to discount stores. Assuming the average return rate for bestsellers of 35%, our estimate of e-commerce retailing costs is far lower than the traditional system. Our estimates do not include any costs associated with stock-outs in the traditional system; the e-commerce model places books not immediately available on back-order for eventual delivery. Purchasing a book via e-commerce or at a bookseller are not entirely equivalent “goods”.  The recreational aspect of visiting a bookshop, for instance, is an important factor. Also, on-line booksellers may have a wider selection than conventional bookstores. 

E-commerce logistics systems involve more reliance upon airfreight service than truck or rail modes. Airfreight requires much higher energy and fuel usage, with corresponding large air pollution emissions. Figure 1 shows comparative supply chain energy effects from trucking, airfreight and rail (CMU GDI 2002). Table 2 shows the use of energy for the trucking, air freight, packaging, fuel production and book production for the traditional and e-commerce retail models.  

Figure 1:  Comparison of Freight Mode Energy Requirements 

In order to quantify the energy impacts associated with the production of the fuel used in passenger vehicles, a producer price of $0.90/gallon was assumed for our calculations. This figure was then combined with the values for the fuel efficiencies of passenger cars and light trucks above to arrive at the dollar amount of fuel used for passenger trips to the bookstores. Using a fleet composition of 35% light trucks and 65% passenger cars [NHTSA 1998], the dollar cost of fuel for one round trip to the bookstore is $0.225. For 286,000 trips to the bookstore, the monetary cost of the fuel used is $64,400. 

Our results indicate significant differences between the retail fulfillment modes, with e-commerce having comparable impacts when compared to the traditional model with returns. The comparison between e-commerce and the traditional model without returns is less clear. Overall, energy from passenger vehicle trips (including fuel production) contributes significantly. By eliminating these trips, energy use is significantly reduced in the e-commerce model. However, the increased air freight and packaging of the e-commerce system outweighs much of the benefits from reduced passenger trips. 

Energy use in the production sectors was estimated using the Economic Input-Output Life Cycle Assessment (EIO-LCA) model [CMU GDI 2002].  The economic costs from Table 1 were used as inputs to the model for the relevant economic sectors.  As seen in Table 2, the e-commerce impacts lie roughly between the zero and 35% return traditional models. In the case of the worst-case e-commerce air scenario, it is only slightly more energy intensive than the traditional system. Results in Table 2 show total results (and direct only effects in parentheses).  Direct effects do not include supply chain energy use, as found in EIO-LCA. Due to data and model uncertainties, none of the four models show significantly different results.  It is clear from Table 2 that the increased energy used for delivery logistics trades off with the passenger pickup component.  Passenger trips are much less efficient than freight logistics networks, thus any energy savings would need to come from reducing the passenger transportation component of e-commerce retailing.  While not detailed in Table 2, total energy use for shipping two books per order are 57 and 46 MJ for traditional and 48 and 44 MJ for e-commerce by air and truck.  Estimated emissions of greenhouse gases range from 4.5-5.3 kg per book in traditional retailing and 5.1-5.7 kg for online.  Greenhouse gases increase relatively in the online method due to increased use of air transportation. 


(35% returns)


(no returns)

E-commerce Air E-commerce Truck
Trucking (w/ returns) 18 (15) 12 (10) 4 (3) 9 (8)
Air N/A N/A 13 (11) N/A
Courier Deliveries N/A N/A 37 (31) 37 (31)
Logistics Subtotal 18 (15) 12 (10) 54 (45) 46 (39)
Book Production 18 (2) 13 (1) 13 (1) 13 (1)
Packaging 3 (<1) 2 (<1) 9 (1) 9 (1)
Passenger Trips 34 (34) 34 (34) N/A N/A
Passenger Fuel Production 5 (3) 5 (3) N/A N/A
Passenger Subtotal 39 (37) 39 (37) N/A N/A
Total 78 (55) 67 (49) 77 (48) 68 (41)

Table 2 
Comparative Total (and Direct) Energy Effects for Book Retailing, per book sold, all units in MJ 

US Case Study Conclusions 

We analyzed a generic scenario for traditional versus E-Commerce retailing of a single commodity, best-selling books. Our analytical approach can be adjusted for different assumptions about shipping distances, return rates or shopping purchase allocations. By altering these critical parameters, E-Commerce can be found to be more or less costly or energy efficient than the traditional system. It is noted that different assumptions about population density (and thus, distances to retail stores) and order sizes significantly change the results. Examples of potential spillover effects from electronic commerce that were not analyzed include structural changes to the economy, substitution of manual or physical processes to digital systems, et cetera [Romm 1999].  Nevertheless, our base analysis case suggests that E-Commerce sales have a cost advantage and comparable energy use and greenhouse gas emissions. Given the assumption of air delivery of e-commerce purchases, and underlying data uncertainties, we conclude that e-commerce is neither more nor less energy intensive than traditional methods. 


Case Study of Energy Requirements from Book Retailing in Japan


Two conditions that should substantially affect the environmental performance of b2c e-commerce are population density, which is related to consumer transport modes and distances, and the amount of goods purchased per order. Japan makes an excellent geographical choice for studying the former factor as it contains both densely populated urban regions with extensive public transport and rural regions where personal automobiles are the main form of transport.  

We review the Japanese book sector in order to set the stage for analysis.  The industry functions under a fixed price system in which discounting below the retail price is not permitted (for domestic books) and the profit margins for bookstores and distributors and fixed at 20% and 10% respectively.2 The central players in the book industry are the distribution companies, who decide the inventories of bookstores and orders made to publishing houses. Of the 7 large distribution firms, the two major ones, Nippan and Tohan, control 80% of the book market. The publishing and bookstore industry is by contrast quite diffuse, with a few large firms but no clear dominating group.  

79% of publishing houses are located in the Tokyo area or outskirts of Tokyo. Transport of books between publisher, distributor, and bookstore is generally contracted out to trucking firms. Shipping from the e-commerce company to consumer is handled by courier services, which are dominated by 3 large firms. Trucking is the favoured mode of transport for book shipments, with little use of rail and essentially no shipments via air. This contrasts with the US case in which air shipments are not


Bulk shipment (lg.)

Personal transport



Bulk shipment (med.)

Bulk shipment (lg.)



Book Store

Reader’s household



Reader’s household




System boundary

Figure 2: Structure of Japanese distribution systems


Simulation of energy use in distribution

This section presents a physical model of energy use for the distribution of books in Japan.  The two systems for e-commerce and traditional retail distribution are depicted pictorially in Figure 2.  The analysis of energy includes the phases falling within the system boundary marked in the figure. In contrast to the US study, the production phase is neglected here. The book supply chain in Japan is managed by a few mega-distributors who practice sophisticated supply chain management.  The existence of a difference in remainders between online and traditional retailers is unclear in this context.  

The four factors included in the simulation of energy use are the fuel used in transport by shipping and courier services, fuel used by the consumer in travel to and from bookstore, energy to produce packaging, and electricity and fuel consumed at the sales point, either at the bookstore or by the consumer at home making an e-commerce purchase.   

In order to evaluate the dependence on population density, distribution and sales in three different regions in Japan were considered: Tokyo, Tochigi and Hokkaido. Tokyo is taken as representative of a densely populated urban region (5,600 inhabitants/km2), Tochigi as a suburban area (310 inhabitants/km2), and Hokkaido as a rural area (68 inhabitants/km2).   

Fuel used in transport by shipping and courier services

The two main pieces of information needed to estimate the consumption of fuel by shipping and courier vehicles are the typical distances between nodes in the distribution network and the type of truck used between each node. This data was gained through a set of interviews with seven major firms involved in publishing, distribution, sales, and transport of books. The basic formula used was: 

Energy per book [M] =  distance [km]  1/truck fuel efficiency [l/km] 

energy content of fuel [MJ/l]  volume share in truck [%], 

where the sum is over legs of the distribution path. In the e-commerce case, shipments between distributor and e-commerce firms are carried in large (10-ton) trucks. At the e-commerce firm’s distribution centre, individual customer orders are placed into small parcels and shipped via courier service. The courier service uses large trucks to transport packages to regional distribution centres, after which small (2-ton) trucks carry to individual’s residences. For conventional retail, the leg connecting distributor and bookstore is handled by large trucks (10-ton) as far as nodal distribution centres, after which shipments were divided and placed in small (2-ton) trucks for delivery to local bookstores. Fuel efficiencies were assumed to be 3 km/l and 5 km/l for large and small trucks respectively. The amount of energy allotted to transport of a book shipment was calculated according to the volume (not weight) fraction of the parcel of compared to the capacity of the truck. In the courier service leg of the e-commerce case, the volume was calculated assuming 2 books are shipped in box of volume 7,290 cm3 (measured from actual boxes used by Amazon.co.jp).  

Fuel used by the consumer in travel to and from bookstore

The mode of transport used and distance travelled by the consumer was estimated based on information on the number of bookstores located in different regions [Regional databases, 1999]. Typical distances between bookstores were calculated assuming stores are on average uniformly distributed in a prefecture. The resulting store-to-store distances are 1 km, 5.2 km and 13.4 km for Tokyo, Tochigi and Hokkaido respectively. The consumer typically travels half this distance to reach the nearest store.  We assumed that the total travel distance was that for a round trip to and from the nearest bookstore, solely for the purpose of purchasing books (i.e. not part of a shopping trip). This is admittedly a crude estimation, only intended identify the order of magnitude of energy use. In general consumers may travel further than the closest store and shopping trips are often combined.  

Given the above assumptions on typical distances, the short distance to the nearest bookstore in Tokyo suggests that walking or bicycle is the preferred mode of transport, while personal automobiles are used in Tochigi (suburban) and Hokkaido (rural) cases. The typical fuel efficiency of automobiles was taken as 13 km/l [EDMC 2001]. 

Energy to produce packaging

Packaging for shipping from distributor to e-commerce warehouse or bookstore is apparently the same: distributor firms report that medium sized cardboard boxes holding about 40 books are used. Bookstores generally put books in light paper bags or wrap in a paper cover for the customer.  E-commerce firms ship books in small cardboard boxes. We obtained representative samples of actual packaging used and measured their weight and volume. The energy used to produce the packaging was calculated according to life cycle process data from the European BUWAL database [BUWAL 1996]. BUWAL data reports that energy to produce 1 kg of cardboard is 25 MJ, and 45 MJ for paper.  Physical characteristics of packaging and estimated energy investment are summarized in Table 3. 

Type Use Weight (g) Energy (MJ)
Medium cardboard box (40 books) distributor to bookstore or e-commerce firm 734 18
Small cardboard box (1-3 books) e-commerce to home 329 8.1
Small paper bag bookstore to consumer 9 .41
Book cover (paper) bookstore to consumer 7 .32

Table 3: Packaging characteristics and energy investment 

Sales point energy

Energy is also consumed at the sales point, either at the bookstore or the home. The bookstore case was calculated combining macro statistics on energy consumption for utilities per unit area in retail stores [EDMC 2001], the total area of bookstore space in Japan (Publishers 2000), and data on number of books sold [Shuppan 2000]. The result of this calculation is that 0.68 MJ of energy for lighting, heating, and air-conditioning was consumed per book.  

Estimation of point-of-sale energy for e-commerce is more difficult as there is little existing data on the differential increase in residential energy consumption due to additional time spent at home. We approached the problem by constructing a simple model of residential energy consumption. The purchase of one book via e-commerce was assumed to take 20 minutes, with an additional 10 minutes per additional book purchased. Residential consumption includes electricity used by computers and lighting as well as energy for heating or cooling. Central heating is rare in Japanese homes, climate is generally controllable room-by room on-demand. It is assumed that making an e-commerce purchase involves heating or cooling one room, one third of an average residence. The energy consumption for computer use is taken at 65.5 W, which averages consumption of desktop and laptop models (Miyamoto/Tekawa/Inaba 1998). We assumed that lighting of one room typically takes 200W of power. Annual per household use of energy for climate control is 14,250 MJ [EDMC 2001]; this is converted to wattage/room by assuming 12-hour/day usage 365 days/year. The resulting average power consumption for climate control is 303 W per room.   Combining the above results yields that home energy consumption associated with purchase of one book via e-commerce is 0.7 MJ. 

The estimations indicate that point-of-sale energy consumption at the bookstore and home are very similar in scale. This underlines the importance of including increased residential energy consumption in analysis of ICT modes, a factor often neglected. For instance, Romm and collaborators cite a comparison of business energy expenditure of e-commerce and traditional booksellers as evidence that e-commerce is energy efficient [Romm, 1999, 26]. We suggest that the energy savings at the firm level is lost due to increased residential consumption.  

Japan Case Study Results  and Conclusions 

Calculations of the energy use associated with each of the four factors for the case of one book are presented in Table 4. The case of two books per sale is shown in Table 5. The results suggest a crossover in performance as population density changes. The conventional system uses less energy in dense urban regions due to additional packaging and courier fuel use for e-commerce. As the population density decreases, e-commerce saves energy because courier services are apparently more efficient than “shipping” via personal automobiles. Not surprisingly, the energy efficiency per book improves substantially as the number of books in an order increases.  

Unit: MJ   Shipping, Courier Personal transport Package Point-of-


Tokyo E-commerce 0.15 0 8.5 0.7 9.3
  Traditional 0.013 0 0.85 0.68 1.6
Tochigi E-commerce 0.66 0 8.5 0.7 9.9
  Traditional 0.10 6.8 0.85 0.68 8.5
Hokkaido E-commerce 3.1 0 8.5 0.7 12
  Traditional 0.37 14 0.85 0.68 16

    Table 4. Per book energy use (1 book per purchase)

Unit: MJ   Shipping,


Personal transport Package Point-of-sale Total
Tokyo E-commerce 0.075 0 4.5 0.5 5
  Traditional 0.013 0 0.65 0.68 1.35
Tochigi E-commerce 0.33 0 4.5 0.5 5.5
  Traditional 0.10 3.4 0.65 0.68 5
Hokkaido E-commerce 1.5 0 4.5 0.5 6.5
  Traditional 0.37 7 0.65 0.68 9

Table 5. Per book energy use (2 books per purchase) 

Although the study addresses a single commodity and a particular region, the results may reflect some general lessons on the conditions under which b2c e-commerce becomes an environmentally friendly transport technology. Packaging is evidently a key issue, both in terms of the energy investment to produce it and for courier services to realize efficient load factors. Minimization of packaging should thus be a priority for e-commerce firms. Population density and number of books per order are also important factors. Both are addressable to some extent through consumer choice, although it is unclear to what extent behaviour can be changed. This is the long-standing challenge of green consumerism. Policies and prices for shipping give e-commerce firms some influence over the size of an order.


Model Comparisons and Conclusions


In this section we compare and integrate the results of the two case studies. The EIO-LCA and conventional LCA methods yield qualitatively similar results - i.e. that 'crossover points' exist between traditional and e-commerce enabled book retailing.  This implies that there is no definitive answer as to which sales and distribution method is more energy efficient, rather it depends on the conditions of implementation, in particular the transport modes used by shippers and consumers. E-commerce is most favoured when shipping modes are unaltered (i.e. no added air freight) and consumer automobile travel is substituted by courier services.  

The quantitative results of the two case studies differ greatly. Excluding the production of books, total energy for sales and logistics in the US ranges from 53-63 MJ for one book versus 2-16 MJ for Japan. Some portion of this gap arises from the difference in methodologies and factors considered.  The US study utilises input-output models, which consider the effects across the supply chain from production (e.g. estimates of transportation energy include the fuel cycle).  A comparable Japanese input-output model was not available.  Traditional LCA models only focus on effects from processes included in the analysis.  Thus input-output models will generally lead to higher estimates. However, the main contribution to the gap between results for the US and Japan lies in geographical and social differences between the two countries. Since Japan is more densely populated and smaller than the U.S., distances for shipping and passenger trips to bookstores are significantly smaller. Also, Japan, with its extensive public transportation networks and train “culture”, has a much lower reliance on the automobile. This implies a much larger fraction of shoppers travel by train, bus, bicycle, or foot.  

The differences in composition of the distribution energies in the US and Japan suggest a distinct set of priorities for energy savings. The logistics energy per book in the US compares with or even exceeds the production energy, thus remainder rates affected the result less than might be expected. Avoiding use of air travel and shortening/aggregating travel distances becomes a top priority. For Japan, the lower energy use in transport gives a greater weight to packaging, remainder rates and building energy use.  

As seen above, the US and Japan models differ in data availability, sources, and method.  However, they show similar views of the energy requirements for retail book distribution. In addition, the sensitivities of the models to assumptions like shopping trips make potentially large differences.  The end result suggests that while the actual energy requirements for purchasing books will differ greatly by customer, the overall estimate is that the methods are comparable.  However, estimation to a higher order faces substantial methodological and data obstacles.  

These case studies lead to some additional observations regarding future work.  First, as the relative impacts of other ICT-enabled business/lifestyle models (e.g. telework and telecommuting) are considered, similar 'crossover' effects should be expected. Second, in cases where the model results are comparable, data uncertainties alone might account for the differences.  Finally, since few business processes are explicitly designed to be energy efficient, studies like this are most useful as a comparative benchmark to see whether new business models can be energy efficient.  Customers are unlikely to stop using a particular method merely because of energy issues; however, policy shifts may tend to discourage energy inefficient processes.



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1 Cost calculations in this case study are based on the comparative costs involved in selling a million dollars worth (at production) of 'best seller' books, or roughly 286,000 books at an assumed production cost of $3.50 each. We assume each book is 23 x 6 x 16 cm in size (9 x 2.25 x 6.25 inches) and weighs 1.1 kg (2.4 pounds).

2 This is in contrast to the United States where online retailers aggressively discount prices to gain market share, brand recognition, and loyalty.

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