| Front page | | Contents | | Previous | | Next |

Input/Output analysis - Shortcuts to life cycle data?

12. Non-energy related emissions in IOA

Gregory Norris, Harvard University and University of New Hampshire

12.1 Abstract

Input/Output LCA models can address more than energy-related emissions. They can be expanded to address releases of a wide array of pollutants. They can also include extractive flows of resources from the environment, and non-flow-based environmental impacts such as land use as well. This paper describes briefly the data sources and methods used to date to accomplish these extensions for IO-LCA models in the USA. It includes a discussion of some of the method and data alternatives where relevant, and also indicates some of the sources of uncertainty that arise, based on characteristics of the databases underlying the models.

12.2 Data Sources and their Integration to Create US IO-LCA Models

12.2.1 Economic and Energy Data

Input/Output LCA models in the US make use of the detailed US Input/Output data from the US Department of Commerce’s Bureau of Economic Analysis (BEA), together with Federal data on pollution releases by sector (from the US Environmental Protection Agency, or EPA) and Federal data on fuel-specific energy consumption by sector (from the Department of Energy’s Energy Information Administration). Below a description is provided of the data sources and their use in creating the "LCNetBase" IO-LCA modeling system.

The US Bureau of Economic Analysis’ 1992 detailed Input/Output Accounts provide a starting point for modeling inter-industry flows. The BEA’s "Make" and "Use" tables are used directly in our analysis, to enable tier-by-tier assessment of results. The "Make" matrix reports industry outputs: the value of each commodity produced by each industry. The "Use" matrix reports industry inputs: the value of each commodity used in the production of each industry’s output.

We retained 498 industries from the BEA tables, including government enterprises such as the US Postal Service, and the 488 BEA commodities produced by these industries. For most manufacturing industries, the BEA industries and commodities match the US four-digit Standard Industrial Classifications (SICs) one-for-one. Outside of manufacturing, some BEA industries represent aggregations of 4-digit SICs, while other BEA industries are composed of portions of one or more 4-digit SICs.

Many establishments in the economy manufacture more than one type of product. This product diversity is even more pronounced among the full set of establishments classified within a single Standard Industrial Classification (SIC) category (Streitwieser, 1991). The industries and commodities are created by BEA in order to provide a characterisation of the inputs and outputs of more homogeneous producing units than those which would arise from developing and publishing the tables on a purely SIC basis - that is, simply using the total production and consumption data for all establishments which are assigned to each SIC as the basis for defining industries as SICs.

Next, fuel-specific energy consumption data (in Btu per dollar of sectoral output) is integrated into the system. The US Census of Mining reports fuel-specific energy consumption for the mining industries. Electricity consumption in kWh is also reported for all manufacturing industries (by 4-digit SIC) by the 1992 Census of Manufacturing, as is cost of other purchased fuels. Note that not all purchased fuels are actually combusted; some are used as feedstocks to product production, as in the use of petrochemicals as feedstocks in manufacturing plastics or fertilisers.

The Department of Energy’s Energy Information Administration (EIA) conducts biennial surveys of manufacturing industry energy consumption, by fuel and end-use, and reports both costs and quantities in energy units. The EIA data report the quantities of each fuel combusted. Data for fuel combustion from the 1991 Manufacturing Energy Consumption Survey (EIA 1994) were used in creating the recent version of LCNetBase.

Fuel-specific manufacturing energy combustion data and the fuel-specific census of mining energy consumption data were converted to provide fuel-specific consumption totals by BEA industry. For nearly all manufacturing industries, the mapping from four-digit SIC to BEA industry is one-to-one; in a few cases, multiple SICs are assigned to a single BEA industry. The match-up from SICs to BEA industries for the 1992 Input/Output accounts is published by the BEA.

For the major energy consuming sectors, the Department of Energy’s Manufacturing Energy Consumption Survey (MECS) reports fuel-specific combustion by four-digit SIC. For sectors that consume smaller amounts of energy, MECS reports fuel-specific combustion by 3-digit or 2-digit SIC. These fewer-digit SICs consist of multiple 4-digit SICs. In these cases, the (1991) MECS-reported fuel shares fuel prices for an aggregated sector were combined with the (1992) Economic Census-reported total cost of fuels for each detailed sector, in order to derive estimated fuel-specific combustion quantities by detailed sector. The total fuel-specific combustion within each 2-digit and 3-digit sector will match those reported by MECS.

EIA also reports fuel-specific sectoral prices for the following non-manufacturing sectors: residential & commercial, industrial, transportation, and electric utilities. These prices (for 1992, concurrent with the BEA consumption data in the Input/Output accounts) were used to convert the non-manufacturing BEA industry fuel and electricity consumption data from dollars to energy units (EIA 1993).

12.2.2 Pollution Data

12.2.2.1 Carbon Dioxide

Fuel-specific sectoral energy combustion data are used to calculate fossil fuel-based carbon emissions by sector, using the fuel-specific carbon emissions coefficients at full combustion provided in Table A1 of (EIA 1995). These emissions were converted from metric tons of carbon to metric tons of CO₂, and were then divided by each sector’s 1992 value of product output to obtain CO₂ emission intensities, in units of metric tons of CO₂ per dollar of 1992 product output.

An alternative approach taken by researchers at Carnegie Mellon University is to use directly the Department of Commerce (DOC) data on fuel purchases. This

approach avoids the task of estimating MECS data for suppressed 4-digit industries, but introduces two sources of error and an upward bias. First, whereas MECs reports physical consumption as well as costs, the CMU approach applies average fuel prices to estimate physical quantities purchased based on the DOC fuel cost data (Joshi 2000). Second, assuming that all purchased fuels are combusted entails ignoring feedstock uses, and thus will overestimate emissions. Ax useful research project would be to compare the results from the two approaches and to estimate the uncertainty in each method’s results.

12.2.2.2 Conventional Air Pollutants

A priority set of air pollutants in the US is termed the "criteria air pollutants" because the US EPA has issued air quality criteria which states must monitor, report annually, and take steps to achieve. This set of pollutants includes NO_x, VOCs, SO₂, CO, and particulates. Until recently the monitored measure of particulates was "PM-10" – particulates less than 10 microns in diameter. The upper-bound particle size was recently changed to 2.5 microns: "PM-2.5." As with CO₂, there are two different approaches used to create sector level emissions coefficients for use in IO-LCA models in the US.

For LCNetBase, we use annual emissions inventories for each pollutant and each 4-digit SIC published by the US EPA. These emissions are divided by sector output for the data year to compute the emissions coefficient (in tons per million dollars of output). As with CO₂ emissions, Carnegie Mellon researchers use the Census of Manufacturers data on fuel costs together with average fuel prices and average combustion emission factors for various fuels published by the US EPA to estimate emissions (Joshi 2000). Again, it would be useful to compare the results of these two approaches and estimate their uncertainties.

12.2.2.3 Toxic Releases

The US EPA publishes annually the Toxic Release Inventory (TRI), which includes media-specific emissions inventories for a set of toxic pollutants for a subset of companies required to report. Reporting requirements have changed over the years since the first TRI reporting year of 1987. For 1996, the most recently published emissions data, facilities required to report are those classified in the manufacturing sectors, with the equivalent of 10 or more full-time employees, whose manufacturing or processing of a TRI chemical exceeds 25,000 pounds per year, or whose level of "otherwise using" the chemical is 10,000 pounds per year. Reporting facilities must only report concerning those TRI-listed chemicals for which the usage thresholds are exceeded (EPA 1999).

Reporting facilities must report the amounts of each reported chemical released on-site to the air, water and land as well as injected underground, resulting from routine releases as well as accidental and fugitive releases. The amounts can be estimated; they are not required to be measured. Companies report the basis of each estimate as either:

Thus, the TRI data provide at once a very powerful source of data for IO-LCA, but also a data source with considerable uncertainties. Factors contributing uncertainty include:

TRI chemical emissions factors are developed for use in IO-LCA by dividing reported annual release totals for each 4-digit SIC by the annual product output from those sectors. This requires that economic output data for the TRI reporting year be used, or else an additional source of error will be introduced. It also introduces an as-yet un-quantified downward bias in the emissions factors for TRI chemicals, both for the reporting sectors and for the supply chain as a whole.

12.3 Summary

IO-LCA models can and have been expanded to include pollution sources beyond those directly tied to energy. They can also be expanded to include the extraction/consumption of resources besides energy as well. These expansions make IO-LCA a more powerful and comprehensive tool. They also introduce a variety of data quality and uncertainty issues that tend to be unique to each data set and its relationship to the economic data at the core of IO-LCA models. These sources of uncertainty have not been studied or quantified to date.

12.4 References

EIA 1993: Energy Information Administration, US Department of Energy. Annual Energy Review 1992, Table 3.7; DOE/EIA-0384(92). Washington, DC. June.

EIA 1994: Energy Information Administration, US Department of Energy. Manufacturing Consumption of Energy 1991, DOE/EIA-0512(91). Washington, DC. December.

EIA 1995: Energy Information Administration, US Department of Energy. Emissions of Greenhouse Gases in the United States 1987-1994. DOE/EIA-0573(87-94). Washington, DC. October.

EPA 1999: 1987-1996 Toxics Release Inventory, CD-ROM Documentation. EPA-749-C-99-003. US Environmental Protection Agency, Washington, DC.

Joshi, Satish, 2000: "Product Environmental Life Cycle Assessment Using Input-Output Techniques". Journal of Industrial Ecology, vol. 3, number 2&3, pp. 95-120.

Streitwieser, Mary, 1991. "The extent and nature of establishment-level diversification in sixteen US manufacturing industries," Journal of Law and Economics, Vol. XXXIV (2), Part 2, October 1991, pp. 503-534.

U.S. Department of Commerce, Bureau of Economic Analysis, 1998. Benchmark Input-Output Accounts of the United States, 1992. Washington, DC: U.S Government Printing Office.

| Front page | | Contents | | Previous | | Next | | Top |