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In recent years alternative automobile power technologies have received increased attention from OEM's, special interest groups, and the public. Plausible power technologies now include internal combustion engines, batteries, fuel cells, and a variety of hybrid technologies. The merits of each of these technologies as a means to move personal and fleet transportation into the next century have been highly debated. One technology that has emerged as a viable alternative to the internal combustion engine is the fuel cell. Considering arguments on all sides of the debate, the authors describe the results of a systematic, focused examination of the sustainability of fuel cells for transportation and discuss strategies for sustainable technology design. Sustainable technologies are those that contribute to preserving or improving societal quality, the environment, and the economy for future generations.

This paper considers the issues and provides some lessons learned with respect to implementing a life cycle environmental assessment (LCEA) and environmental cost analysis (LCEC) program within a major DoD system acquisition. The latest revision of Directive 5000.2, Mandatory Procedures for Major Defense Acquisition Programs, requires, among other things, that life cycle environmental aspects be considered early in the design process[1]. Further, the 1995 Defense Appropriations Act, Section 815, requires that environmental costs be an integral part of the system life cycle cost analysis. For this effort project personnel, with the guidance of the Office of the Program Manager staff, developed an LCEA/LCEC Program, trained design teams on the elements of the program and prepared a data collection template to assist in the ongoing data collection effort.

Providing confidence in life-cycle inventories (LCI) is dependent on being able to understand the source and extent of uncertainties in data and in the results produced with the data. From a situation several years ago of nearly no methodology for data quality considerations to a future where sophisticated data modeling approaches allow decision makers to obtain quantitative indications of the differentiability of alternatives, the science and art of data quality assessment are advancing rapidly. This paper provides perspective on why and how data quality issues are critical to successful implementation of LCI results and an overview of how practitioners are responding to the need for enhanced data quality assessment procedures. These procedures range from incorporation of individual data quality indicators to statistically-based models for estimation of parameter distributions. Careful consideration of data quality can markedly improve the interpretation and utility of LCIs.