Air pollution is a threat to the environment and human health. Freight trucking in particular is the main source of freight transportation emissions. Heavy-duty trucks emit large amounts of toxic air pollutants that cause serious diseases and harm public health. In addition, heavy-duty trucks emit great amounts of greenhouse gas (GHG), which is the leading cause of global warming. Despite increased environmental restrictions on air pollution and rising trucking greenhouse gas emissions in the past decades, no economic study has examined the potential GHG and air pollution reductions in the trucking sector and the associated private abatement costs to the industry. This study accounts for GHG emissions and toxic air pollutants in measuring and evaluating efficiency and productivity for the trucking industry in the 48 contiguous states. Moreover, the private costs of abatement to the industry were also estimated. When only GHG was incorporated in the production model, the results showed that each state could expand desirable output and reduce GHG by an average of 11 percent per year between 2000 and 2007. The Malmquist-Luenberger productivity indexes showed that omitting or ignoring GHG in trucking service production yielded biased estimates. On the other hand, due to increased environmental regulations, most of the toxic air pollutants decreased dramatically between 2002 and 2005. The analytical results showed that inefficiency decreased during this period. The private costs of abatement averaged $73 million per state in 2005. When GHG and six toxic air pollutants were incorporated in the production model, the estimated private abatement cost was $76 million per state, which was equivalent to 0.7 percent of the industry output in 2005.

This research focuses on the development of multi-criteria tools for measuring and analyzing the impacts of recurring and non-recurring congestion on freight corridors in the Portland Metropolitan Area. Unlike previous studies, this work employs several distinct data sources to analyze the impacts of congestion on Interstate 5 (I-5) in the Portland Metropolitan Area: global positioning system (GPS) data from commercial trucks and Oregon DOT corridor travel-time loop data and incident data. A new methodology and algorithms are developed to combine these data sources and to estimate the impacts of recurrent and non-recurrent congestion on freight movements' reliability and delays, costs, and emissions. The results suggest that traditional traffic sensor data tend to underestimate the impacts of congestion on commercial vehicles travel times and variability. This research also shows that congestion is not only detrimental for carriers and shippers costs but also for the planet due to major increases in GHG emissions and for the local community due to large increases in NOx, PM, and other harmful pollutants. The methodology developed throughout this work has the potential to provide useful freight operation and performance data for transportation decision makers to incorporate freight performance measures into the planning process.