Session: Transport

Modeling Passenger and Freight Transportation in Input-Output Analysis: Challenges and Potential Solutions
Christopher L. Weber,^*   Carnegie Mellon University
H. Scott Matthews,   Carnegie Mellon University

The past few years have seen a proliferation of the use of economic input-output analysis (IOA) as a tool for life cycle assessment (LCA). While IOA has several advantages for LCA, such as aggregate data availability, cut-off error minimization, and temporal efficiency, its problems are also severe. Perhaps most important are its problems related to price variation of commodities between and within sectors and sectoral aggregation error, two uncertainties which are difficult to quantify but likely very important for most IO modeling. These problems may be pronounced for transportation sectors, for several reasons: freight and passenger price variability, the aggregation of passenger and freight transport together, allocation issues between passengers and freight, and the treatment of international transport and imported transport services are a few.

This study seeks to typify and quantify typical uncertainties in modeling passenger and/or freight transport using input-output based LCA (IO-LCA). National level input-output and transportation data from various US sources, including benchmark input-output accounts, transport and passenger logistics data, and international trade data, are compared systematically to detail uncertainties related to differential pricing and aggregation. We conclude that both types of error are likely very significant in typical IO-LCA approaches, and thus, results for transporation-related LCI studies using IOA should be treated with some degree of caution.

However, IOA does have its advantages for modeling transportation logistics, as it is able to show logistics at every level of a product’s supply chain from raw material extraction to final delivery when using detailed IO tables. We propose a mixed-unit input-output approach that may solve some of the above problems while maintaining the advantageous total supply chain delineation of IOA. However, as we discuss, this approach will have its own difficulties in model construction and data uncertainty. As in many cases, a hybrid LCA approach may provide the least uncertainty overall.

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