Impacts of biodiesel blending on freight emissions in the Midwestern United States

We use a combination of petroleum–diesel models, datasets and tools along with biodiesel-specific corrections to create a roadway-level emissions inventory capable of evaluating spatial, temporal and scale aspects of fuel distribution options for the Midwestern US. Specifically, we compare the emissions of a year-round “low-blend” biodiesel implementation scenario, already under consideration in a variety of states, with a more strategic summer-only, interstate-only “high-blend” scenario. Our results indicate that spatial and seasonal distribution decisions do affect the overall emissions impacts of any biodiesel deployment, even those at low-blend levels. However, we also finds that changes in emissions due to biodiesel are considerably smaller than those anticipated from improvements to engine and control technologies.     

The merits of accelerated depreciation for promoting investment in clean transport technologies: A simulation study in the Australian rail freight industry

This article ascertains the viability of promoting accelerated depreciation for newly acquired locomotives and other rolling stock as a means of encouraging technological investment in more efficient and environmentally friendly assets. The study uses a tax-adjusted asset replacement model to evaluate the merits of accelerated depreciation, and then compares the outcomes with alternative incentive schemes. It also examines what would occur if various schemes were used simultaneously.     

Analysis of the effectiveness of control measures to mitigate road dust emissions in a regional network

The effectiveness of control measures to reduce road dust emissions is analyzed using a year’s data of road dust emissions collected with a mobile sampling platform and a survey of road maintenance practices in the Lake Tahoe Basin of Nevada and California US. Attributes such as sweeping practices, anti-icing, shoulder improvement, pavement condition, trackout, and abrasive material from road segments were analyzed with a feature subset selection algorithm. Street sweeping was found to be an effective means of controlling dust emissions from roads. Road dust from dirty tertiary roads served as a continuous source of suspendable material for adjacent high-speed roads in the winter time. To be most effective, emission control strategies require that not only primary roads, but all roads be swept after snow storms to recover applied abrasive material.     

Developing green fleet management strategies: Repair/retrofit/replacement decisions under environmental regulation

The considerable cost of maintaining large fleets has generated interest in cost minimization strategies. With many related decisions, numerous constraints, and significant sources of uncertainty (e.g. vehicle breakdowns), fleet managers face complex dynamic optimization problems. Existing methodologies frequently make simplifying assumptions or fail to converge quickly for large problems. This paper presents an approximate dynamic programming approach for making vehicle purchase, resale, and retrofit decisions in a fleet setting with stochastic vehicle breakdowns. Value iteration is informed by dual variables from linear programs, as well as other bounds on vehicle shadow prices. Sample problems are based on a government fleet seeking to comply with emissions regulation. The model predicts the expected cost of compliance, the rules the fleet manager will use in deciding how to comply, and the regulation’s impact on the value of vehicles in the fleet. Stricter regulation lowers the value of some vehicle categories while raising the value of others. Such insights can help guide regulators, as well as the fleet managers they oversee. The methodologies developed could be applied more broadly to general multi-asset replacement problems, many of which have similar structures.     

To kill a real option – Incomplete contracts,real options and PPP

This paper is concerned with the implications of public–private partnership agreements for the execution of expansion options in road infrastructure. More specifically, it analyzes the expansion of an existing two-lane road in Sweden, and examines the real options created by an intermediate type of road with three lanes. Interpreting the results from real option analysis in the light of incomplete contract theory, this paper finds that external congestion costs might necessitate public ownership to ensure a social optimal outcome in public–private partnerships.     

Redefining activity types: Who participates in which leisure activity?

This paper analyzes the activity choices of individuals and the links between socio-demographics, daily schedules and activity attributes using a new activity choice framework. Activities are first clustered into groups based on their salient attributes, such as duration, frequency, flexibility, planning times, and number of involved persons, rather than their functional types (work, leisure and household obligations), using a K-means cluster technique. This led to the creation of several new activity groups such as “long, temporally fixed, personally flexible activities”, “short and flexible activities”. These activity groups form the choice set for the mixed logit activity choice modeling structure developed for the leisure activities in the second part of the paper. The model results reveal the significant relationships between socio-demographics, temporal characteristics, and characteristics of the schedules on leisure activity choice. The results demonstrate how changing demographics and other activities in individuals’ schedules may affect the nature of the leisure activities and present the substitution and complimentary effects that these new activity groups have on one another.     

Step-tolling with price-sensitive demand: Why more steps in the toll make the consumer better off

Most dynamic models of congestion pricing use fully time-variant tolls. However, in practice, tolls are uniform over the day, or at most have just a few steps. Such uniform and step tolls have received surprisingly little attention from the literature. Moreover, most models that do study them assume that demand is insensitive to the price. This seems an empirically questionable assumption that, as this paper finds, strongly affects the implications of step tolling for the consumer. In the bottleneck model, first-best tolling has no effect on the generalised price, and thus consumer surplus remains the same as without tolling. Conversely, under price-sensitive demand, step tolling increases the price, making the consumer worse off. The more steps the toll has, the closer it approximates the first-best toll, thereby increasing the welfare gain and making consumers better off. This indicates the importance for real-world tolls to have as many steps as possible: this not only raises welfare, but may also increase the political acceptability of the scheme by making consumers better off.     

A hedonic test of the effects of the Alternative Motor Fuels Act

Under the Alternative Motor Fuels Act (AMFA), vehicles that run on ethanol, methanol, or natural gas get extra credits in the calculation of Corporate Average Fuel Economy (CAFE). This paper uses hedonic techniques to examine the effect of production of alternative-fuel vehicles (AFVs) on the implicit price of fuel economy. This study finds that, after AFVs came to market, the marginal value of fuel economy from companies producing them decreased. This finding suggests that manufacturers who produced AFVs were willing to offer a lower price for fuel economy, because automakers had an additional way to achieve fuel economy standards beyond improving the fuel efficiency of conventional cars. These findings bolster the argument that a major role of the AMFA credit for AFVs is to allow automakers to increase their production of fuel-inefficient vehicles.     

An analysis of destination choice for opaque airline products using multidimensional binary logit models

We investigate how customers respond to an opaque airline product offered by a European carrier. In this opaque product design, customers are randomly assigned to travel to one of approximately ten destinations; however, for a fee they may exclude one or more destinations from the choice set (or a particular package design) prior to learning which destination they will travel to. We use a multidimensional binary logit model to predict the probability that one or more alternatives will be chosen by a customer. Results show that customers are more likely to pay to exclude destinations located close to the origin airport and destinations that speak the same language as the origin airport. Length of stay, cost of living at the destination, and measures of destination attractiveness are also found to be significant predictors for some package designs. Based on these findings, we offer general recommendations for how to design opaque packages for airline customers.     

Remoteness and accessibility in the vulnerability analysis of regional road networks

This paper considers the development of a method for network vulnerability analysis which considers the socio-economic impacts of network degradation and seeks to determine the most critical locations in the network. The method compares the levels of remoteness (or its inverse, accessibility) of localities within the study region, on the basis of the impacts of degradation of the road network on a recognised accessibility/remoteness index that can be applied to each and every location within the region. It thus extends the earlier work on accessibility-based vulnerability analysis which was limited to assessment of impacts on selected nodes in a network. The new method allows study of impacts on both specified locations (which do not have to be represented as network nodes) and the region as a whole. The accessibility/remoteness index is defined so that an accessibility surface can be calculated for the region, and the volume under this surface provides an overall measure of accessibility. Changes in the volume under different network states thus reflect the overall impacts. The method is applied to a rural region in south east Australia.