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

Abstract

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 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 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 inventoriesand 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.