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The remainder of this document begins with a discussion of components of the general approach used to generate the cost-effectiveness estimates summarized in Chapter One. This discussion includes an outline of data sources used to seed the analysis, and a description of the process used to generate the range of analytical scenarios. The discussion reviews model components, and associated assumptions and limitations. Key components include elements essential to represent impacts on travel demand, emission intensity, project lifetimes, project costs, and associated emission rates represented through the use of MOVES2010b.
Chapter Two of this document concludes with a review of each project type included in the analysis. For each project type, the discussion outlines the inputs and steps required to generate cost-effectiveness estimates. The discussion presents a representative sample calculation of cost-effectiveness estimates for a subset of the relevant pollutants associated with each project type (e.g., cost-effectiveness estimates for NOx and PM2.5), based on the range of inputs identified for use within the analysis. In cases where distinct processes were used to generate cost-effectiveness estimates within a given project type (e.g., distinct types of transit projects, diesel retrofit scenarios involving heavy vehicles and construction equipment), multiple processes and examples are presented. For each project type, the discussion concludes with a summary table of median cost-effectiveness estimates identified in the analysis.
This section outlines the process used to generate the analytical scenarios used in the estimation of cost-effectiveness by project type. Each analytical scenario involves a specific representation of a project, defined primarily in terms of costs, travel demand, travel demand impacts and emission rates. The specification of scenarios was based upon multiple data sources. The fullest representations of project-level data were found in data from the CMAQ project database, including the two most recent CMAQ assessment studies (2008 Assessment Study, 2014 Assessment Study), and in additional project summaries from States and localities containing data consistent with CMAQ project summaries. Additional key information was found in existing reviews of mobile emission mitigation projects, in particular Multi-Pollutant Emissions Benefits of Transportation Strategies (FHWA, 2006).
A literature review and series of internet searches identified additional information used to populate scenarios in the analysis, including studies of specific policies (e.g., carsharing in San Francisco, bikesharing in Washington, DC, electric vehicle charging stations in Minnesota) and reviews of technological effectiveness (e.g., effects of idle-reduction technologies). Industry documentation offered additional insight into project costs (e.g., school bus replacement costs, vanpool costs) and demand impacts.
Lastly, information from government sources offered integral components of analytical scenarios. Key examples include annual VMT and idling estimates from EPA's Diesel Emissions Quantifier (DEQ). In addition, for scenarios involving off-road activity that cannot be represented in MOVES (chiefly scenarios involving construction equipment), the DEQ was applied independently to yield cost-effectiveness estimates within the analysis. Other critical information was identified via models operated by MPOs (e.g., assumptions regarding project-level factors and impacts for infrastructure projects).
To generate individual scenarios in the analysis, the required model inputs (e.g., project costs, travel demand, travel demand impacts, emission rates) were specified from available sources (e.g., CMAQ assessment studies). In cases where the full set of required information was available for a given case, cost-effectiveness estimates were generated by dividing the project cost by the scenario-specific estimates of emission impacts. The emission impacts were identified as the difference in the products of travel volumes and unit emission rates under the project relative to the status quo across the project lifetime.
For example, consider a simple case of a one-year, $10,000 project that reduces annual passenger vehicle VMT by 50,000, at a prevailing average travel speed of 35 miles per hour, at an estimated CO emission rate of three grams per mile. To estimate the cost-effectiveness of the project with respect to carbon monoxide, one would divide the project cost by the estimated reduction in CO. The reduction in CO is estimated as:
(Change in VMT) x (CO Emissions per Mile)
Figure 7. Equation. Estimation for CO Reduction.
With a 50,000-mile annual reduction in vehicle travel and an estimated CO emission rate of three grams per mile, the project would yield a reduction of 150 kilograms of CO, or approximately one-sixth of a ton of CO (0.16535 ton). At a cost of $10,000, the cost-effectiveness of the project would be estimated as $10,000 divided by 0.16535 ton, or $60,479 per ton ($0.07 per gram).
When full information is not available for a given case, representative values from related cases or the literature were included to fill in missing details. For example, if a project lifetime was not specified for a given infrastructure project, and if a common project lifetime was observed for related projects, the common value would be substituted into the analysis for the infrastructure project.
Additional scenarios were generated by substituting inputs from one documented project in place of values for other documented projects. For example, if a range of (scaled) project costs are observed across otherwise comparable projects, it would be reasonable to allow for an analysis of hypothetical cases in which alternative, feasible project costs apply to a given emission impact from a project. Such substitution was applied for multiple model inputs (e.g., demand impacts, vehicle mixes affected) to expand the range of scenarios.
Where applicable, a given analytical scenario was expanded into a range of scenarios by varying one or more inputs to represent plausible alternatives. For example, for a given analytical scenario with a particular project cost, travel demand and associated travel speed, alternative scenarios could be generated by using the same project cost and travel demand, but also varying the associated travel speed (e.g., representing congested arterials, uncongested arterials, and uncongested highways). This process was repeated as appropriate to allow for variations in factors including vehicle age (e.g., for diesel retrofits), impacts vehicle use (e.g., to test a range of plausible demand patterns or sensitivities), and road types (e.g., urban versus rural arterials or highways).
By representing a range of values for key inputs, the analytical process is capable of estimating a range of scenario-specific cost-effectiveness estimates that represent the variability of plausible outcomes across proposed projects within a given project type. Two key factors to consider in this regard were identified during the analytical review process: utilization rates and switching factors. Utilization rates represent the uptake of new or improved services and infrastructure (e.g., new bus routes, improved intersections). For a given level of potential demand (e.g., number of potential transit users, daily private vehicle users), variations in utilization rates will lead to different quantities of demand (e.g., transit trips, vehicle miles of travel). In turn, variations in demand will have proportional impacts on cost-effectiveness.
For example, consider a transit project with 100,000 potential transit riders, in which five percent of potential riders are projected to utilize a new transit service, with an estimated impact of a reduction of one ton of NOx over the lifetime of the project. At a project cost of $100,000, the project would have an estimated cost-effectiveness of $100,000 per ton of NOx reduced. If a project with the same characteristics were implemented in an area with 100,000 potential riders but with ten percent of project riders utilizing the service, the estimated reduction of NOx would be twice as large (two tons) and the estimated cost-effectiveness would be doubled ($50,000 per ton).
Similarly, switching factors represent the share of users by status quo mode (e.g., private vehicle drivers, public transit users). For projects centering on reductions in private vehicle VMT, the share of users switching from private vehicles is of critical importance; users coming from other modes would not result in reductions in private vehicle VMT. Hence, for a given impact of changes in private vehicle VMT on a focal pollutant (e.g., per-mile emission rates for CO for private vehicle use), variations in switching rates will lead to different changes in private vehicle usage. In turn, variations in private vehicle use will have proportional impacts on cost-effectiveness.
For example, consider a ridesharing project with 10,000 potential participants, in which 50 percent of potential participants are projected to switch from the use of private vehicles (with the remainder coming from public transit, for whom there would no estimated emissions reduction), with an estimated impact of a reduction of 100 tons of CO over the lifetime of the project. At a project cost of $500,000, the project would have an estimated cost-effectiveness of $5,000 per ton of CO reduced. If a project with the same characteristics were implemented in an area where 25 percent of potential participants are projected to switch from the use of private vehicles, the estimated reduction in CO would be half as large (50 tons), and the resulting estimated cost-effectiveness would be halved as well ($10,000 per ton).
Median cost-effectiveness measures generated across the range of analytical scenarios for a given project type are presented at the end of each subsection in Analytical Results.
The general structure of the analysis centers on linking key inputs from external sources (e.g., CMAQ project proposals, projects consistent with CMAQ proposals) to emission estimates from analysis in MOVES2010b. Key inputs in the generation of estimates of cost-effectiveness (measured in dollars per ton of pollutant reduced) are shown in Table 2.
Baseline travel demand estimates and the range of sensitivity estimates serve to quantify the impact of a given project type on travel demand by vehicle type. Technological effectiveness measures (e.g., percentage of emissions captured by a diesel retrofit) represent the share of pollutant emissions that would be captured over a given volume of travel demand or engine use (e.g., hours of idling). Representative travel speeds and road types are used to link specific emission rate estimates from MOVES2010b to estimated impacts on travel volumes. For example, impacts at a relatively low average speeds, which involve frequent acceleration and deceleration, will result in different per-mile emission rates compared to the same travel volume at free-flow speeds on the same type of road, due to the impact of those frequent accelerations and decelerations. Lastly, project lifetimes expand annualized estimates of emission impacts across a relevant timeframe.
The estimation of cost-effectiveness was driven by a relatively simple specification: the project cost divided by the estimated impact on emissions of a given pollutant. The estimated emission impacts can be represented in general terms as:
[(Travel Volume under the Project)*(Unit Emissions Rate under the Project)]-[(Travel Volume under the Status Quo)*(Unit Emissions under the Status Quo)]*Project Lifetime
Figure 8. Equation. General Terms to Represent the Estimated Emissions Impacts.
This was kept simple in the example for CO cost-effectiveness estimation above, but was complex in some scenarios. For example, projects involving different travel speeds on different lanes (e.g., under managed lanes) require calculating impacts for each relevant traffic netwo7rk component (e.g., differences between emissions under the project and the status quo for all types of lanes). Similarly, projects involving impacts on different vehicle types (e.g., under increased transit service) require calculating impacts across all vehicle types (e.g., differences between emissions under the project and the status quo for passenger vehicles and buses).
Information on baseline (i.e., status quo) travel demand is required for all projects, including projects that do not affect travel volumes (e.g., baseline VMT estimates are required for analyses of diesel retrofit projects). For most projects, associated travel speeds are required to identify per-mile emission rates to apply to the evaluation of a project. The availability of reliable travel demand information may vary across projects, and may include total trips by network link and vehicle type, total vehicle hours or miles of travel, and patronage for transit services. Furthermore, the most appropriate data may be disaggregated by time of day to enable analyses involving peak versus off-peak travel.
For the range of projects that focus on in-vehicle technologies, fuel and maintenance, the analysis requires assumed parameters for the expected technical effectiveness of the focus of the project. As a simple example, consider a scenario in which an idle reduction policy is projected to reduce the volume of a given pollutant in average heavy duty truck trips by ten percent. The analysis would involve estimating total heavy duty diesel truck pollutant emissions within a given scenario using MOVES2010b, and then estimate the reduction in emissions resulting from the policy as equal to ten percent of the baseline heavy duty diesel truck emissions from the MOVES2010b analysis.
For the subset of projects with a focus on encouraging mode shift (i.e., from light-duty vehicle to public transit, carpool, vanpool, rideshare or non-motorized travel), the analysis requires assumed parameters for the sensitivity of demand by mode with respect to the project. The assumed relationship between the effects of the project on demand by mode is used to evaluate the net effects on travel behavior.
For example, a given representative project may involve an assumption that a measure will lead to five percent of daily commute trips moving from light-duty vehicle to express bus. In this case, the share of daily commute trips would be scaled down by five percent, with those trips offset by an appropriate volume of express bus trips (modeled separately).
For the range of projects involving meaningful changes to transit service, the analysis requires information to represent both changes in demand arising from improved facilities, and tangible changes to the transit fleet. With respect to changes in demand, the analysis requires estimates of mode shift resulting from the availability of improved facilities. With respect to changes in the transit fleet, the analysis requires information on changes to the level of service and corresponding changes in transit demand.
The 2008 CMAQ Phase 1 Final Report notes that some states and MPOs report cost-effectiveness estimates that reflect only the relationship between the amounts of CMAQ funds applied to a project relative to the full emission impacts of a project. That is, such measures attribute all emission reductions for projects to the proportion of total project funding that is comprised of CMAQ funds, essentially designating other funds as having no cost-effectiveness at all. The view taken in this research is that it was important to represent the cost of the entire project, rather than just the associated CMAQ funds. A key objective of this effort is to represent the relative cost-effectiveness of CMAQ-eligible projects, independent of the relative share of CMAQ funds that a given project receives. The approach taken in this research is the same as in FHWA (2008), representing total project costs within cost-effectiveness measures, without differentiating by funding source.
Another time-related factor to control for is the duration of benefits (FHWA, 2008). Different projects have different operational lifetimes (e.g., infrastructure projects are likely to be longer-lived than operational programs). The analysis specifies representative project lifetimes across which benefits are applied, consistent with project lifetimes reported in existing CMAQ projects and the literature. As an example of the range of time frames covered within this approach, consider Table 3 below (FHWA, 2008, p. 55), which offers a summary of project lifetimes specified in a CMAQ evaluation under SAFETEA-LU:
EPA s mobile source emissions model (MOVES2010b) was used in the development of the cost-effectiveness tables to generate estimates of emissions for the range of project types evaluated within the analysis. MOVES2010b was designed by the EPA for the purposes of modeling on-road air pollution emissions from motor vehicle activity. (EPA, 2012) The model analyzes all on-road motor vehicle classes, and allows users to incorporate significant local-level detail. MOVES2010b estimates key criteria pollutants and their precursors, including the pollutants evaluated within this research.
There is a broad range of key data that must be specified within MOVES2010b analysis of emission impacts for the representative projects selected for this research, as summarized in Table 4 below. (EPA, 2012) National-average values for the variables in Table 4 were selected as the default values for the analysis within MOVES2010b for most cases; the primary exception was extreme-temperature cold-start technologies, for which a range of data representative of Fairbanks, Alaska was specified.
| Category | Input | Impact on |
|---|---|---|
| Weather | Local temperature | Most pollutants |
| Relative humidity | NOx | |
| Vehicle Fleet | Population of vehicles for 13 types | All pollutants |
| Distribution of vehicle ages for 13 types | All pollutants | |
| Travel Demand | Annual VMT by vehicle type | All pollutants |
| VMT by road type | All pollutants | |
| Travel Speed | Distribution of average speed | All pollutants |
| Ramp Fraction | Share of VHT on ramps | All pollutants |
| Fuel Type and Technology | Distribution of energy source across diesel, gasoline, CNG and electricity by vehicle type and model year | All pollutants |
| Fuel Formulations | Volumes of fuel formulations consumed, defined in terms of: Reid vapor pressure, sulfur level, ethanol volume, additives | All pollutants |
| Fuel Market Share | Proportions of diesel, gasoline, CNG and electricity consumed by fuel formulation | All pollutants |
The range of vehicle types modeled within MOVES2010b is represented in Table 5:
Within each of the vehicle types listed in Table 5, MOVES is capable of modeling distinct estimates of emission rates by fuel type, including gasoline, diesel and natural gas. Separate emission rates for appropriate subsets of vehicle types were identified in MOVES for relevant analyses (e.g., school bus emission rates for diesel retrofits of school buses, heavy truck emission rates for intermodal projects). Fleet-average emission rates were identified for projects influencing a range of vehicle types (e.g., traffic flow improvements).
The set of required information by vehicle type in MOVES2010b is multi-dimensional, covering the population of vehicles by age and fuel source; and annual VMT by road type. The distribution of vehicles by age was of key relevance for scenarios involving engine replacement, vehicle replacement and vehicle technologies. In cases involving either engine/vehicle replacement or vehicle technologies, the age of the replaced vehicle is a critical factor in the volume of emissions abated via replacement (positively, through higher per-mile emission rates as vehicles age, and negatively, through decreased project lifetimes as vehicles age).
EPA (2012) clarifies that the distribution of travel speeds used within a given analysis should be defensible. In this analysis, the travel speeds linking MOVES model runs and calculations of emission impacts were specified both directly from real-world projects and allowed to vary across meaningful ranges as a means of sensitivity analysis.
For example, a given project type could involve examples with prevailing average travel speeds of 30 and 35 miles per hour. In this analysis, the relevant parameters from project descriptions would not only apply to emission rates from MOVES model runs at 30 and 35 miles per hour, but also to emission rates from MOVES model runs at slower and faster speeds. This enables the analysis of similar projects applied under different conditions.
Importantly, specifications of travel speeds are not required for scenarios that are not linked to specific travel conditions (e.g., diesel retrofits, idle reduction). In such scenarios, the specification of a fleet-average travel profile (in cases that apply to all travel, such as retrofits) or no travel at all (in cases that apply to starting, idling or charging, such as idle reduction), are appropriate.
Central assumptions for the analysis are listed below:
A key policy-related assumption is that all emissions are accounted for equally across project lifetimes; that is, a ton of pollutant emissions abated in 2015 is treated in the analysis as equivalent to the same ton of pollutant emissions abated in, say, 2025. The purpose of this assumption is to treat all cohorts experiencing emission impacts the same, rather than favoring groups in particular time periods. The alternative would be to discount emissions to a present value. Such an approach could be appropriate if the marginal social benefit of emission mitigation is expected to change significantly over time; this was not expected to be the case for the project lifetimes governing most, if not all, projects in the analysis.
As mentioned earlier in this section, another key assumption is that the cost-effectiveness of a given project for a given pollutant is independent of the project s impacts on other pollutants. That is, the cost-effectiveness measures do not involve any weighting across pollutants, consistent with FHWA (2008) (i.e., an assumption of zero shared costs across pollutant reductions. Hence, we assigned the total cost of a project to each pollutant category, and then estimated the cost per ton reduction for each pollutant; these measures, in turn, are essentially upper-bound estimates of costs per ton.
For example, consider a project with a cost of $100,000 that leads to a reduction of one ton of VOCs and two tons of NOx. Our approach would result in cost-effectiveness estimate of $100,000 per ton for VOCs and $50,000 per ton for NOx.
FHWA (2008) selected their methodology for two key reasons. Firstly, FHWA believed that it was difficult to select one weighting system that was representative at the national level, due to variations in the relative impacts of pollutants by location. Secondly, because some projects are targeted at reductions in a focal pollutant, FHWA believed that weighting systems could obscure the relative effectiveness of different strategies at reducing different pollutants. We agree with both points, and hence chose to generate separate cost-effectiveness tables for each pollutant.
There are three related assumptions governing the representation of project costs in the analysis. Firstly, the full project cost is assigned to projects, rather than the share of project costs covered by CMAQ funds. This assumption was imposed to preserve comparability across scenarios. Ultimately, cost-effectiveness estimates should reflect how effectively a given project type achieves reductions in pollutant emissions (information of paramount importance to State and local decision-makers). Representing only the share of CMAQ funds associated with individual project examples would result in estimates that attribute all pollutant reductions to CMAQ funds (and attribute no pollutant reductions to alternative sources of funds).
The full project cost is assumed to be incurred in the first year of the project. This represents the timing of the obligation of funds from the CMAQ Program toward projects (i.e.,as lump sums). This assumption reflects cost-effectiveness from the perspective of the social cost of funds, rather than at the local, transactional level.
The full project cost is also assumed to incorporate all relevant costs (i.e., capital, operating and maintenance). The estimates of project costs that were used within the analysis do not generally differentiate between components assigned to capital costs versus operation and maintenance costs; the corresponding assumption of funds being applied to all project costs was selected for consistency with the data.
Other central assumptions relate to the representativeness of individual analytical scenarios, the range of analytical scenarios, and the estimates yielded within MOVES2010b. Each analytical scenario included in the analysis was identified within documentation on CMAQ projects, other related projects, a literature review and information provided by industry and related groups. The range of scenarios included in the analysis does not include all identified candidate cases, however. Rather, the analysis does not include information from cases that were either considerable outliers (e.g., infrastructure-intensive projects with limited impacts compared to less-intensive projects) or described in vague terms. As a result, the analysis assumes that the range of analytical scenarios to evaluate includes not only best-case scenarios, but also scenarios that are relatively effective for a given project type. Relatively weakly-performing scenarios can feasibly be found for any project type, and do not add much to the information gained within the analysis, at the cost of adding noise to the results.
The range of analyses in MOVES2010b yielding emission rate estimates are also based on a set of assumptions which, in turn, conditions the range of cost-effectiveness estimates. The most critical assumptions within the MOVES analysis include: the composition of relevant components of the vehicle fleet (e.g., shares of passenger cars and trucks on highways, proportions of heavy trucks by age, annual VMT by vehicle type and age across road types), the appropriate drive schedule for a given scenario (i.e., changes in vehicle speed across modeled trips), and the spatial coverage for a given scenario (e.g., project-level, national-average).
Lastly, the analysis generates unique cost-effectiveness estimates for each analytical scenario. This raises an important question of how best to characterize cost-effectiveness for each project type, based upon a given range of scenario-specific estimates. After comparing alternative approaches to representing the cost-effectiveness estimates, the preferred approach was to represent cost-effectiveness in terms of median cost-effectiveness estimates by project type (and, in the case of the detailed results presented in Chapter Two, by project sub-type when applicable). Median estimates, while commonly similar to mean estimates, are not influenced by the magnitude of outliers (i.e., scenarios with unusually high or low estimated costs per ton of pollutant reduced). Rather, in this analysis the median is the closest available measure of a representative (i.e., middle-of-the-pack) project. Best-case estimates were also considered; however, just as mean estimates are prone to being distorted by unusually poorly-performing projects, best-case estimates may overstate the effectiveness of a given project type. For example, diesel retrofits of relatively old long-haul trucks may perform much better than diesel retrofits on average, but old long-haul trucks may represent a very small share of vehicles eligible for retrofits under a given project.
The range of analytical scenarios is intended to cover neither the full range of potential outcomes within a project type, nor the full range of potential projects. The analysis centers on a snapshot of data, which limits the scope of inference that can be drawn. Difficulties in identifying representative project examples for some project types limited the range of potential projects included in the analysis. Hence, the range of project types included in the analysis is targeted at representing an informative view of the relative performance of predominant (and potentially predominant) project types across the range of pollutants in the study, rather than serving as a census of all projects eligible for CMAQ funding.
The analysis is also limited by the scope of factors represented within the analytical scenarios. That is, the results are strictly limited to being representative of projects with prevailing factors consistent with the examples evaluated in the analysis. Hence, States and MPOs considering projects that include features outside the boundaries of the scenarios analyzed in this research should consider external information to confirm the implications of this analysis. This is consistent with a broader limitation: States and MPOs may have access to a range of information and operate under distinct sets of constraints or objectives. Critically, this analysis is targeted at representing a meaningful comparison of the general cost-effectiveness of competing project types, but is not targeted at serving as a single, definitive source in this area. It is expected that States' and MPOs' project- and agency-specific knowledge will serve a critical role in concert with the information presented in this document.
As discussed in the review of assumptions above, a maintained assumption in the analysis is that the estimated project costs cover the full extent of capital, operating and maintenance costs. If projects include operating costs that are not represented within the estimated total project cost (e.g., in cases where only capital costs are evaluated within the application process), estimates of cost-effectiveness would be biased upwards (i.e., a given reduction in pollutant emissions would be associated with a larger total cost than the capital cost associated with the estimate).
The costs for project types centering on user-specific technologies or policies (e.g., diesel retrofits, employee transit passes) are represented as the per-unit costs, rather than expected total costs for a bundle of units including administration and installation fees. Hence, the estimated cost-effectiveness for such project types is essentially a lower bound value; administration and installation costs would raise the effective cost per ton reduction of a given pollutant.
The analysis assumes constant annual impacts across project lifetimes, unless variable information across years was available (e.g., changes in expected emission rates calculated within MOVES2010b). This assumption would bias cost-effectiveness estimates downwards (i.e., lower cost per ton) if impacts would be expected to decrease over time. However, the strongest performing project types in the analysis tend to be shorter-lived, and hence the tendency of any bias would be toward decreasing the relative differences in cost-effectiveness across project types.
It is also important to acknowledge that cost-effectiveness with respect to reducing pollutant emissions and congestion is not necessarily the primary reason to implement a given project. Rather, there can be a wide range of benefits provided by projects (e.g., greenhouse gas mitigation, reductions in fuel consumption, safety improvements). In this analysis, we are focusing on the two central issues relevant to the CMAQ program, air quality improvement and reductions in traffic congestion. While other benefits may be of critical importance to State and local organizations, benefits other than reductions in traffic congestion and pollutants associated with CMAQ Program objectives are outside the scope of this analysis.
This section covers the process used to identify cost-effectiveness estimates for each project type in the analysis, along with a summary of the range of cost-effectiveness estimates (i.e., estimates of cost-effectiveness for PM2.5, NOx, VOCs, CO and PM10) for each project type. Within the discussion for each project type, this section reviews:
The discussion for each project type also includes one or more representative sample analytical scenarios, presented in terms of:
For each sample analytical scenario, examples demonstrate how a subset of cost-effectiveness measures (generally represented in terms of PM2.5 and NOx) are calculated. The use of a subset of calculations was selected for brevity; the same process was used to calculate cost-effectiveness measures for all pollutants in the analysis for a given project type.
The examples presented in the remainder of this document are simplified examples targeted at demonstrating the processes used to generate cost-effectiveness estimates. The examples do not present individual scenarios from the analyses, although some inputs are in common with those used in the analysis. The discussion for each project type concludes with a summary table of the full range of median cost-effectiveness estimates identified in the analysis.
The summary tables present median cost-effectiveness estimates for PM2.5, NOx, VOCs, CO and PM10; in cases where a given project type does not affect all five pollutants in the analysis, results are presented for the subset of pollutants affected by the project type. The estimates are also split by distinct project sub-types (e.g., splitting dust mitigation into street sweeping and road paving) where applicable.
Diesel retrofits involve technologies applied to vehicles and equipment operating on diesel fuel, to reduce the volume of target pollutants emitted while in operation. The two primary types of diesel retrofits evaluated in this analysis are diesel particulate filters (DPFs) and diesel oxidation catalysts (DOCs). DPFs and DOCs reduce some (but not all) PM2.5 and CO emissions by capturing these pollutants before they exit the exhaust system of the vehicle or equipment.
In addition, diesel retrofits can reduce the volume of PM10 emissions, although specific emission impacts for PM10 versus PM2.5 were not available. In cases where general impacts of particulate matter were available, the same impact was assumed for PM10 as for PM2.5 (reflecting the ability to capture fine particulate emissions at least as large as PM2.5). In cases where only PM2.5 impact estimates were available (i.e., for analyses of diesel retrofits of construction equipment), no impacts on PM10 were estimated.
The range of expected performance for DPFs specified within the list of EPA verified technologies includes reductions of: 85%-90% of PM and 75%-90% of carbon monoxide (CO). The range of expected performance for DOCs specified within the list of EPA verified technologies includes reductions of: 20%-26% for PM and 28%-50% for CO. Additional documentation on DPFs and DOCs indicated similar ranges, confirming the validity of the EPA summary data as inputs to the analysis. The lower and upper values within the ranges of technological effectiveness were used to help establish a meaningful range of cost-effectiveness estimates.
No direct estimates of reductions in VOCs were published by EPA. However, DPFs and DOCs reduce hydrocarbon emissions, which are the predominant component of VOC emissions by diesel engines. In this analysis, diesel retrofit reductions in VOCs were assumed to be equal to hydrocarbon reductions. The expected performance of VOC reductions identified by EPA for heavy vehicles is 90% for DPFs and 50% for DOCs. For all scenarios of DPFs and DOCs applied to construction equipment, the estimated emission reductions from the DEQ were applied directly.
In the analysis, the effects of DPFs and DOCs were investigated for:
Steps required to conduct the analysis of diesel retrofits of heavy-duty trucks and buses include:
The MOVES runs yielded estimates of emission rates (in grams per mile) for CO, PM2.5 and PM10, by vehicle type and model year. That is, a given retrofit technology (e.g., one specific DPF), was estimated by MOVES to have distinct impacts on emission rates depending upon the type and age of the vehicle receiving the retrofit.
Lower- and upper-bound project lifetimes and retrofit technology costs (from $1,000 to $2,000 for DOCs and from $10,000 to $20,000 for DPFs) were selected to generate lower- and upper-bound cost-effectiveness estimates in conjunction with the lower- and upper-bound values of technological effectiveness. The lower bound for project lifetime was specified as five years (i.e., an assumption that the vehicle that was retrofit would last from 2015 through 2019 if it were not replaced), and the upper bound was specified as 11 years (i.e., the vehicle that was retrofit would last from 2015 through 2025), following from the range of project lifetimes identified in the literature review.
To estimate individual cost-effectiveness for each vehicle type/model year/road type combination in the analysis, the estimated cost for a given technology was divided by the product of the estimated change in a given emission rate (i.e., with retrofit versus without), the assumed annual volume of VMT for the vehicle, and project lifetime. This yields a value of dollars per gram of pollutant abated over the project lifetime, which can then be converted to dollars per ton abated. The median cost-effectiveness estimate is then identified as the 50th-percentile value across the set of cost-effectiveness estimates generated using the process described above.
The steps required to conduct the analysis of diesel retrofits of construction equipment, are distinct, due to a lack of construction equipment within MOVES. Instead of applying (unavailable) emission rate estimates for construction equipment from MOVES, EPA's Diesel Emissions Quantifier (DEQ) was used within the analysis of construction equipment. The required analytical steps for analysis using the DEQ include:
The specification of annual usage is distinct for construction equipment compared to heavy vehicles; construction equipment activity is specified more appropriately in terms of hours of usage than in VMT. The retrofits were assumed to take place in the base analysis year (2015).
Cost estimates for the specific technologies selected for the DEQ were collected from suppliers, where available, and compared to broader estimates from the literature to confirm representativeness. All equipment was assumed to be used at levels equivalent to default values in the DEQ. Lastly, the DEQ requires the specification of one state for the location of the project. Multiple model runs were conducted across states with climate attributes (i.e., average temperature, heating degree days, cooling degree days, morning and evening relative humidity) at or near the national average. The emission impact estimates calculated using the DEQ were insensitive to the selection of states.
As an illustrative example, consider a model year 1999 single-unit short-haul truck with a diesel engine traveling on urban arterials, undergoing a retrofit with a DPF.
In this scenario we assume the following details:
Step One:The reduction in exhaust emissions is calculated by multiplying baseline exhaust emissions by the estimated effectiveness of the DPF in reducing emissions, as summarized in Table 6:
Step Two:The total estimated annual impact on each pollutant is then identified by multiplying the difference between the baseline and retrofit emission rates for the pollutant (i.e., the per-mile emission reduction) in the table above by the average annual travel volume for the vehicle type (27,500 miles):
Step Three: Project lifetime emission impacts are identified by multiplying the annual emission reduction by the project lifetime (in years). All conversions of emissions impacts to (short) tons use the conversion factor of 907,185 grams per ton. For simplicity, assume a constant effect for each year and project lifetime of ten years, yielding:
Step Four:The values in Table 8 above represent the denominator of the cost-effectiveness measures in the example. To identify the cost-effectiveness measures (in dollars per ton), it is necessary to divide the cost of the DPF by the estimates of total ton reductions:
As an illustrative example, consider a case of a model year 1999 construction crane, retrofitted with a DPF in 2012 (the latest retrofit year available in the DEQ).
In this scenario we assume the following details:
Step One:The DEQ reports annual emission totals based on annual hours of operation and emission rates represented in terms of grams per hour. Rather than calculating emission impacts manually based on emission rates and estimated usage, the analysis incorporates the estimates of baseline emissions and emission reductions directly from the DEQ. Applying the selected retrofit technology would reduce the crane emission rates to: 0.0.194 and 0.0587 tons per year of PM2.5 and CO, respectively, as estimated by the DEQ and summarized in Table 10 below:
Step Two: Project lifetime emission impacts are identified by multiplying the annual emission reduction by the project lifetime (7.9 years, as reported by the DEQ in this example). The project lifetime represents the interval over which the project would have an impact, which in this case is the expected remaining years of service for the vehicle:
Step Three: The values in Table 11 above represent the denominator of the cost-effectiveness measures in the example. To identify the cost-effectiveness measures (in dollars per ton), it is necessary to divide the cost of the DPF by the estimates of total ton reductions in Table 11 above:
The median cost-effectiveness estimates for the range of project scenarios for heavy-duty trucks, transit buses, school buses and construction equipment are presented in Table 13 below. The median estimates were identified as the 50th-percentile value for each subset of individual cost-effectiveness estimates reported below:
In all cases except for one (school bus DPF retrofits measured at the mean), retrofit technologies are highly cost-effective relative to other alternatives in mitigating PM2.5, CO and, where estimated, PM10 emissions. Overall, DOCs were estimated to be more cost-effective than DPFs, due to favorable trade-offs between technology cost (DOCs generally cost less than DPFs, as low as one-tenth the cost of DPFs) and technological effectiveness (DPFs are more effective at mitigating emissions, but not sufficiently to overcome differences in cost).
Retrofits of heavy-duty trucks (including both short-haul and long-haul trucks) and transit vehicles were estimated to be reasonably competitive with one another in terms of cost-effectiveness; retrofits of trucks were estimated to be more cost-effective in mitigating CO and PM2.5 emissions, but retrofits of heavy-duty trucks and transit buses were estimated to be roughly equivalent in mitigating PM10 emissions. Retrofits of school buses were estimated to be less cost-effective than retrofits of heavy-duty trucks and transit buses; this appears to be primarily a factor of vehicle usage (estimated annual VMT for school buses of around 15,000, compared to around 50,000 for transit buses and up to 100,000 for heavy-duty trucks).
This section reviews the analysis of replacements of heavy duty vehicle engines. These projects center on substituting new, low-emission engines in place of relatively older, high-emission engines. A basic example of a relevant engine replacement would be substituting a new (model year 2015) engine for a long-haul combination truck in place of a model year 2000 engine. Not only would the MY2015 engine operate free of the effects of long-term wear and tear (unlike the MY2000 engine), but the MY2015 engine would also be designed under more rigorous emission standards for key pollutants such as PM and NOx.
In the analysis, the effects of heavy duty vehicle engine replacements were investigated for:
In all, 512 scenarios were analyzed for heavy-duty trucks, 64 scenarios were analyzed for school buses and 64 scenarios were analyzed for transit buses. The scenarios covered variations by engine age, vehicle size and road type.
The steps required to conduct the analysis of heavy duty vehicle engine replacements include:
The MOVES runs yielded estimates of emission rates (in grams per mile) for each of the pollutants in the study, by vehicle type and engine model year, using national-average travel speed profiles. The estimated annual impacts on pollutants were identified by taking the difference between emission rates for a base-model-year (2015) and focal-model-year vehicle (i.e., a single model year between 1991 and 2006), and multiplying the rates by estimated annual travel volumes from the DEQ.
Lower- and upper-bound project lifetimes and engine replacement costs were selected to generate lower- and upper-bound cost-effectiveness estimates. Consistent with the analysis of diesel retrofits, the lower bound for project lifetime was specified as five years (i.e., an assumption that the original engine would have lasted from 2015 through 2019 had it not been replaced), and the upper bound was specified as 11 years (i.e., the original engine would have lasted from 2015 through 2025 had it not been replaced), following from estimates of heavy-duty truck engine lifetime VMT relative to annual usage estimates.
To estimate individual cost-effectiveness for each vehicle type/model year/road type combination in the analysis, the estimated cost for a given engine replacement was divided by the sum of estimated annual emission impacts across project lifetimes. Each estimated annual emission impact was identified as the product of the estimated change in a given emission rate (i.e., with replacement versus without) and the assumed annual volume of VMT for the vehicle. This yields a value of dollars per gram of pollutant abated over the project lifetime, which can then be converted to dollars per ton abated.
As an illustrative example, consider the replacement of a model year 1999 diesel school bus engine with a model year 2015 engine, for a bus traveling on rural roads.
In this scenario, we assume the following details:
Step One: Replacing the MY1999 engine with a MY2015 engine would lead to the following reductions in per-mile emissions of CO and PM2.5:
Step Two: Each of the estimated per-mile emission impacts is multiplied by the assumed annual travel volumes for the vehicle to identify annual emission impacts:
Step Three: The estimated annual impacts are then summed across the project lifetime to identify the total emission impacts of the engine replacement (457,050 grams, or 0.504 tons of CO, and 62,340 grams, or 0.069 tons of PM2.5), as shown in the bottom row of Table 15 above.
Step Four: The values identified in Step Three represent the denominator of the cost-effectiveness measures in the example. To identify the cost-effectiveness measures (in dollars per ton), it is necessary to divide the cost of the engine replacement by the estimates of total emission impacts:
The median cost-effectiveness estimates for the range of scenarios for replacements of heavy-duty vehicle diesel engines are presented in Table 17 below:
This section reviews the analysis of idle reduction strategies (IR), including truck stop electrification projects. These projects center on the use of technologies to provide power to heavy-duty trucks when the vehicles are not in motion. By providing means to power heavy-duty trucks that do not rely on idling, IR can support shifts to lower-emission energy consumption by heavy-duty trucks. Additionally, IR reduces localized community and driver exposure to diesel engine emissions. Also, plug-in truck stop electrification may enable refrigerated trailers to plug in rather than operating a small non-road engine.
Key IR technologies include auxiliary power units (APUs), overhead ducting systems (chiefly, IdleAire) and plug-in electric power and heating and cooling systems (e.g., Shorepower). The set of available project information centered on plug-in systems and IdleAire projects; each of these project sub-types were included in the analysis.
In the analysis, the effects of IR projects were investigated at the heavy-vehicle-fleet-average level for combinations of heavy vehicle model years and road types. The central emission information for the analysis came from MOVES model runs, which reported emission rates for vehicles at idle (in grams per hour), by model year (weighted by the share of vehicles in operation within each model year) and road type. In all, 101 IR scenarios were analyzed.
The steps required to conduct the analysis of IR projects involving plug-in systems include:
The MOVES runs yielded estimates of emission rates (in grams per hour) for each of the pollutants in the study, by model year and road type, using national-average travel profiles. The estimated annual impacts on pollutants were identified by multiplying the estimated effectiveness of IR technology (e.g., a 60-percent reduction in NOx emissions at idle per device per hour) by the number of idling hours reduced per year and the per-hour emission rates for vehicles at idle.
Lower- and upper-bound values for device utilization rates (15 percent and 60 percent per hour), impact of idling activity (reduction of 25 percent of hoteling and reduction of 100 percent of hoteling) and project costs ($4,500 and $11,500 per space) were used to identify lower- and upper-bound cost-effectiveness estimates. A constant, 15-year project lifetime was assumed.
To estimate individual cost-effectiveness for each model year/road type combination in the analysis, the estimated cost for a given project was divided by the sum of estimated annual emission impacts across project lifetimes. Each estimated annual emission impact was identified as the product of the estimated change in a given emission rate (i.e., with the use of idle reduction versus without) and the assumed annual volume of idling activities for vehicles. This yields a value of dollars per gram of pollutant abated over the project lifetime, which can then be converted to dollars per ton abated.
The analysis of IR projects involving IdleAire was conducted primarily using outputs from the DEQ, and included the following steps:
As an illustrative example, consider the use of an IdleAire device by model year 2000 heavy-duty trucks traveling on urban unrestricted (i.e., highway) roads.
In this scenario, we assume the following details:
Step One: Shifting MY2000 heavy-duty trucks using the facility from 100 percent idling to 40 percent idling (i.e., using the facility 60 percent of the time) would lead to the following annual reductions in emissions of NOx and PM2.5:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios for idle reduction strategies are presented in Table 21 below:
| Pollutant | Cost-Effectiveness |
|---|---|
| PM2.5 | $76,342 |
| PM10 | $51,139 |
| CO | $20,724 |
| NOX | $2,040 |
| VOCs | $122,587 |
The analysis of extreme-temperature cold-start (ETCS) technologies projects center on the use of technologies to mitigate the inefficiencies of starting vehicles at low temperatures; for the purposes of this analysis, the relevant temperature range was from -40 degrees to zero degrees Fahrenheit.
The most prevalent technology with supporting information useful for analysis was engine block heaters, which serve as the representative technology in the analysis. Engine block heaters are a plug-in device that warms engines above ambient temperature, resulting in vehicle start emissions comparable to starts under non-extreme conditions.
In the analysis, the effects of ETCS projects were investigated at the fleet-average level for a range of vehicle types, including:
The central emission information for the analysis came from MOVES model runs, which reported emission rates for vehicles at startup (in grams per start), by vehicle type and ambient temperature (-40, -20 and zero degrees Fahrenheit), and estimates of the effectiveness of relevant technologies from Alaskan projects involving block heaters. National average fleet composition estimates by vehicle type were used to seed the analysis, to represent an assumption that users of block heaters would be distributed consistently with the composition of the national vehicle fleet. In all, 132 ETCS scenarios were analyzed.
Key variables to account for within the analysis include ambient (extreme cold) temperature and the amount of time vehicles are out of operation before starting (i.e., the soak time). Three alternative ambient temperatures were selected (in degrees Fahrenheit): 0, -20, and -40, the latter of which represents the lower bound of expected cold start conditions within the United States (i.e., winter in Fairbanks, Alaska). The upper bound of soak time (greater than 12 hours) was selected for the analysis, to represent cold starts following overnight parking. Estimates of emission reductions under the use of block heaters were identified by multiplying cold-start emission rates (per start) from MOVES by estimates of the number of cold starts per year and estimates of proportional reductions in emissions from cold-start technologies, as identified in a project involving the Municipality of Anchorage (reductions of up to 60%).
The steps required to conduct the analysis of ETCS projects include:
The MOVES runs yielded estimates of emission rates (in grams per start) for each of the pollutants in the study, by vehicle type and ambient temperature, using national-average travel profiles. The estimated annual impacts on pollutants were identified by multiplying the estimated effectiveness of ETCS technologies (e.g., a 50-percent reduction in PM2.5 emissions at startup) by the number of cold starts per year and the per-start emission rates by vehicle type and ambient temperature.
Lower- and upper-bound values for usage rates (60 and 120 annual cold starts), project lifetimes (5 and 10 years), and project costs ($250 and $500 per block heater) were used to identify lower- and upper-bound cost-effectiveness estimates.
To estimate individual cost-effectiveness for each vehicle type/ambient temperature combination in the analysis, the estimated cost for a given project was divided by the sum of estimated annual emission impacts across project lifetimes. Each estimated annual emission impact was identified as the product of the estimated change in a given emission rate (i.e., with the use of ETCS technology versus without) and the assumed annual volume of cold starts for vehicles. This yields a value of dollars per gram of pollutant abated over the project lifetime, which can then be converted to dollars per ton abated.
As an illustrative example, consider the use of a block heater for a passenger vehicle, making 120 starts in zero-degree weather.
In this scenario, we assume the following details:
Step One: Using a block heater during 120 zero-degree starts would lead to the following annual reductions in emissions of NOx and PM2.5:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios for all vehicle types and ambient temperatures are presented in Table 25 below:
This section reviews the analysis of projects involving improvements to intersections, including signalization improvements and re-purposed lanes (i.e., left-turn lanes). These projects focus on the use of technological and engineering approaches to improve the flow of traffic through intersections and along corridors. The analyses of intelligent transportation systems scenarios were conducted using outputs from MOVES2010b and project-level inputs from CMAQ projects, Multi-Pollutant Emissions Benefits of Transportation Strategies (MPEBTS), and documentation by Curbed L.A. and San Bernardino Associated Governments. Emission rate data were identified in national-average-fleet-level MOVES runs for passenger vehicles.
Distinct to other project types, each of the intersection improvement scenarios involved a specific improvement in travel speeds (or reduction in delay, in the case of left-turn lanes), generally around five miles per hour (from bases ranging from 15 to 40 miles per hour). In all, 20 scenarios were included in the analysis.
The steps required to conduct the analysis of intersection improvement projects include:
In the analysis, the effects of intersection improvement projects were investigated at the fleet-average level for passenger vehicles. Emission impacts (in grams per mile) were identified in MOVES as the difference between emissions under pre- and post-implementation travel speeds, both estimated as the product of per-mile passenger vehicle emission rates and VMT reductions per mitigated trip, and project lifetimes (15 or 20 years, depending upon the specification of the scenario). Most projects did not indicate expectations of increased VMT under higher travel speeds; for these scenarios, no increase in VMT was assumed. Some projects did not specify costs; for the corresponding analysis, per-mile and per-signal costs from other scenarios were applied as appropriate.
To estimate individual cost-effectiveness for each scenario in the analysis, the estimated cost for a given project was divided by the sum of estimated annual emission impacts across project lifetimes. Each estimated annual emission impact was identified as the product of the estimated change in a given emission rate (i.e., the change from pre- to post-implementation) and the assumed annual travel volumes for vehicles. This yields a value of dollars per gram of pollutant abated over the project lifetime, which can then be converted to dollars per ton abated.
As an illustrative example, consider a scenario involving ten new signals added along a three-mile urban corridor.
In this scenario, we assume the following details:
Step One: Improving the average travel speed from 15 miles per hour to 20 miles per hour would lead to the following per-mile reductions in emissions of NOx and PM2.5:
Step Two: The per-mile emissions rate impact is multiplied by annual VMT along the corridor to identify the annual emission impact:
Step Three: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Four: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 30 below:
This section reviews the analysis of projects involving efforts to encourage mode shift for heavy-duty truck freight (e.g., from truck to rail, from truck to barge). Key projects identified include an intermodal freight facility in San Joaquin, California; two maritime scenarios including the Brooklyn Marine Terminal and Red Hook Container Barge, and one case of extending railroad access to a port at the Columbia Slough in Portland, Oregon.
In the analysis, the effects of freight and intermodal projects were investigated at the national-average fleet level for heavy-duty trucks. The central emission information for the analysis came from MOVES model runs, which reported emission rates for heavy-duty trucks (in grams per mile), across a range of average travel speeds representing different travel conditions (ranging from 15 to 30 miles per hour on arterials and at 50 miles per hour on highways). In all, 16 freight and intermodal scenarios were analyzed.
The steps required to conduct the analysis of freight and intermodal projects include:
To estimate individual cost-effectiveness for each scenario in the analysis, the estimated cost for a given project was divided by the sum of estimated annual emission impacts across project lifetimes. Each estimated annual emission impact was identified as the product of the estimated emission rate (i.e., the change from pre- to post-implementation) and the assumed annual reduction in travel volumes for vehicles. This yields a value of dollars per gram of pollutant abated over the project lifetime, which can then be converted to dollars per ton abated.
As an illustrative example, consider a scenario involving the use of barges to mitigate heavy-duty truck travel within a metropolitan area.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-trip emission rates by the number of affected trips:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 34 below:
| Pollutant | Cost-Effectiveness |
|---|---|
| PM2.5 | $4,153,174 |
| PM10 | $2,864,417 |
| CO | $315,485 |
| NOx | $248,854 |
| VOCs | $2,570,012 |
There are four distinct types of transit projects in the analysis:
Park and ride projects focus on the provision of new park and ride lots to encourage transfers from light-duty vehicle to public transit. Transit facility and amenity improvement projects center on improving the experience of transit users, in turn stimulating demand for travel by public transit.
Transit service projects center on direct support of transit services, supporting demand for travel by public transit. Consistent with the full range of projects in the analysis, the full range of relevant costs were considered when evaluating transit service projects, rather than focusing on the subset representing CMAQ funding. This is of particular relevance with respect to operating assistance; projects involving operating assistance were represented as having equivalent project costs to projects involving greater levels of financial support, to enable like-with-like comparisons of the impacts of transit service on emissions across all transit service projects.
Subsidized transit fare programs are targeted at stimulating shifts to public transit at times of peak environmental need through the use of temporary discounts on fares, such as during periods with high ozone levels.
In the analyses of all transit projects, the key inputs included:
The estimated emission impacts centered on shifts of travel via light-duty vehicle to transit. Emission impacts were identified as the product of per-mile emission rates and VMT totals across mitigated light-duty-vehicle trips (less additional bus emissions), and project lifetimes. In all, 68 transit project scenarios were analyzed, including 20 park and ride projects, 12 transit service amenity improvement projects, 15 transit service expansion projects, and 21 subsidized transit fare projects.
The steps required to conduct the analysis of transit projects include:
As an illustrative example, consider a scenario involving a new park and ride lot to encourage transfers from light-duty vehicle to public transit.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-trip emissions by the number of affected trips:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
As an illustrative example, consider a scenario involving the installation of a new traveler information system.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-trip emissions by the number of affected trips:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
As an illustrative example, consider a scenario involving the addition of a new transit route.
In this scenario, we assume the following details:
Step One: Annual emission benefits are identified by multiplying per-trip light-duty vehicle emissions by the number of offset trips:
Step Two: Annual emission impacts are identified by subtracting new annual bus emissions from the annual emission benefit identified in Step One:
Step Three: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Four: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
As an illustrative example, consider a scenario involving a fare-free program for ozone action days.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-trip emissions by the number of affected trips:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of transit project scenarios are presented in Table 48 below:
This section reviews the analysis of bicycle and pedestrian projects. The analysis focused on infrastructure projects supporting walking and bicycling in place of travel by light-duty vehicle. Sample calculations of relevant projects include sidewalks, crosswalks, bicycle lanes on existing roads and bicycle and walking paths separated from existing roads. There were no assumed emission impacts involving public transit; both additional public transit trips chained to new bicycle and walking trips and changes from transit to bicycle or walking trips were assumed to have a negligible effect on transit vehicle emissions. In all, 48 bicycle and pedestrian scenarios were included in the analysis.
The key inputs for the analysis of bicycle and pedestrian projects include:
The steps required to conduct the analysis of bicycle and pedestrian projects include:
As an illustrative example, consider a new bicycle path along an existing roadway.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-trip emissions by the number of affected trips:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 52 below:
| Pollutant | Cost-Effectiveness |
|---|---|
| PM2.5 | $3,179,371 |
| PM10 | $1,268,478 |
| CO | $19,060 |
| NOx | $150,235 |
| VOCs | $684,883 |
This section reviews the analysis of employee transit benefit projects. The two types of employee transit benefit projects considered in the analysis were:
In both types of projects, employees receive compensation intended to encourage mode shift from light-duty vehicle to public transit. A notable difference between the two types of projects is that parking cash-out may be associated with more discrete changes in driving activity, in cases where transit pass recipients could feasibly access employer-provided parking intermittently. In all, 36 employee transit benefit project scenarios were analyzed.
Key inputs for the analysis of employee transit benefit projects include:
The steps required to conduct the analysis of employee transit benefit projects include:
As an illustrative example, consider a project involving subsidized transit passes provided by employers.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-trip emissions by the number of affected trips:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 56 below:
| Pollutant | Cost-Effectiveness |
|---|---|
| PM2.5 | $6,140,209 |
| PM10 | $2,859,391 |
| CO | $36,202 |
| NOx | $296,490 |
| VOCs | $1,382,295 |
These projects are distinct to other intersection improvements in the analysis, in that roundabouts involve a clear focus on infrastructure improvements rather than the use of signalization technology in conjunction with physical changes to intersections. In all, 52 roundabout scenarios were included in the analysis.
An additional positive factor supporting the implementation of roundabouts is the potential for significant reductions in crash rates at project sites. For example, the CMF Clearinghouse reveals that roundabouts may be expected to reduce crash rates by between 44 and 87 percent (for all crash types, according to the top-rated study in the CMF Clearinghouse). The crash reductions reported by the CMF Clearinghouse indicate that safety benefits could comprise a large share of total project benefits for roundabout projects at locations with relatively high crash rates.
Key inputs to the analysis of roundabout projects include:
The steps required to conduct the analysis of roundabout projects include:
As an illustrative example, consider a project involving the construction of a new roundabout.
In this scenario, we assume the following details:
Step One: Annual pre- and post-implementation emissions are identified by multiplying per-trip emissions by the number of affected trips under the per- and post-implementation travel speeds:
Step Two: Annual emission impacts are identified by subtracting pre-implementation emissions from post-implementation emissions (a negative difference is shown below as a positive benefit):
Step Three: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Four: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 61 below:
| Pollutant | Cost-Effectiveness |
|---|---|
| PM2.5 | $16,686,148 |
| PM10 | $7,552,437 |
| CO | $114,251 |
| NOx | $2,958,769 |
| VOCs | $4,338,299 |
Carsharing projects center on offering access to vehicles owned and maintained by third parties (e.g., cities) for intermittent trips best served by light-duty vehicles. Access to shared vehicles provides alternatives to reduce overall usage of a light-duty vehicles by households, and in some cases, enables households to carry out travel activities while reducing the number of cars owned by households, both of which may result in decreases in VMT through eliminating some discretionary trips and mode shift to public transit).
Information on carsharing projects was identified through a review of carsharing project documentation and supporting literature (e.g., Cervero et al., 2006). In all, 48 carsharing scenarios were included in the analysis.
Key inputs for the analysis of carsharing projects include:
The steps required to conduct the analysis of carsharing projects include:
As an illustrative example, consider a project involving a new carsharing project.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-mile emission rates by the number of affected trips under the relevant travel speed:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 65 below:
| Pollutant | Cost-Effectiveness |
|---|---|
| PM2.5 | $7,668,684 |
| PM10 | $3,524,324 |
| CO | $40,919 |
| NOx | $319,608 |
| VOCs | $1,698,827 |
Similar to carsharing projects, bikesharing projects center on providing incentives to shift travel mode from light-duty vehicle to bicycle for some trips (rather than reducing the number of cars owned by households), by offering access to bicycles owned and maintained by third parties (e.g., cities) for intermittent trips that can be served via bicycle. Information on bikesharing projects was identified through a review of bikesharing project documentation, with a focus on the Washington metropolitan area s Capital Bikeshare. In all, 24 bikesharing scenarios were included in the analysis.
Key inputs for the analysis of bikesharing projects include:
The steps required to conduct the analysis of carsharing projects include:
As an illustrative example, consider a project involving a new bikesharing project.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-mile emission rates by the number of affected trips under the relevant travel speed:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 69 below:
| Pollutant | Cost-Effectiveness |
|---|---|
| PM2.5 | $24,686,369 |
| PM10 | $9,996,978 |
| CO | $145,393 |
| NOx | $1,217,644 |
| VOCs | $5,369,399 |
These projects center on the provision of infrastructure to support the use of electric vehicles in place of conventional light-duty vehicles. Information on electric vehicle charging infrastructure (EVCI) projects was difficult to identify; information from a project in Minnesota and supplementary information from Vermont formed the basis of the analysis. In the analysis, it was assumed that there are no emissions associated with the use of electric vehicles. In all, 6 EVCI projects were analyzed.
Key inputs for the analysis of EVCI projects include:
The steps required to conduct the analysis of EVCI projects include:
As an illustrative example, consider a project involving a new EVCI project.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-mile emission rates by the number of affected trips under the relevant travel speed:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 73 below:
These projects center on the provision of equipment or personnel to advise or re-route drivers during incidents of non-recurring congestion (e.g., accidents, special events). Information on incident management projects was obtained from CMAQ assessment studies (2008 Assessment Study, 2014 Assessment Study) and supplementary project information on equipment used within incident management projects (chiefly, variable message signs). In all, 18 incident management projects were included in the analysis.
Key inputs for the analysis of incident management projects include:
The steps required to conduct the analysis of incident management projects include:
As an illustrative example, consider a project involving the provision of variable message signs along a corridor subject to non-recurring congestion.
In this scenario, we assume the following details:
Step One: Annual emission impacts are identified by multiplying per-hour emission rates by the number of affected trips involving time at idle:
Step Two: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 77 below:
| Pollutant | Cost-Effectiveness |
|---|---|
| PM2.5 | $2,990,667 |
| PM10 | $2,788,516 |
| CO | $10,718 |
| NOx | $167,771 |
| VOCs | $171,503 |
Dust mitigation projects are unique within this analysis, in that their sole impact is on PM10. There are two main types of dust mitigation projects represented in the analysis: road paving, and street sweeping projects. Road paving projects center on adding a paved surface on top of dirt roads, to mitigate the level of PM10 raised into the local troposphere by vehicle travel. Street sweeping projects center on the direct removal of foreign objects and contaminants from roadways, including PM10. Information on both types of dust mitigation projects was identified within CMAQ assessment studies (2008 Assessment Study, 2014 Assessment Study). In all, 14 dust mitigation projects were included in the analysis.
Key inputs for the analysis of dust mitigation projects include:
The steps required to conduct the analysis of dust mitigation projects include:
As an illustrative example, consider a project involving a street sweeping project.
In this scenario, we assume the following details:
Step One: The annual emission impact is identified by multiplying daily emission impacts by the number of days per year the project is active:
Step Two: The estimated annual emission impact is multiplied by the project lifetime to identify project-level emission impacts:
Step Three: The project cost is divided by the estimated project-level emission impact to yield the cost-effectiveness estimate:
The median cost-effectiveness estimates for the range of scenarios are presented in Table 81 below:
Natural gas fueling infrastructure (NGFI) projects have hypothesized impacts on PM2.5, PM10 and VOCs, by encouraging shifts in heavy-duty vehicle travel from diesel-powered vehicles to lower-emission, natural-gas-fueled vehicles. However, MOVES2010b does not include VOC emission rates for vehicles fueled by natural gas. Hence, the analysis of NGFI projects focuses on PM2.5 and PM10 impacts. Furthermore, shifting travel to vehicles fueled by natural gas may lead to increases in NOx emissions, limiting the useful scope of NGFI projects to areas either without the need to curb NOx emissions or with projects with offsetting NOx reductions sufficient to offset NOx increases under NGFI.
Information on NGFI projects was identified within project-level data from non-CMAQ project that are consistent with CMAQ funding criteria (i.e., there were no similar NGFI project within CMAQ assessment studies). In all, 40 NGFI projects were included in the analysis.
Key inputs for the analysis of NGFI projects include:
The steps required to conduct the analysis of NGFI projects include:
As an illustrative example, consider a project involving a new natural gas fueling station, targeted at serving local buses.
In this scenario, we assume the following details:
Step One: The annual emission impact per vehicle is identified by multiplying per-mile emission impacts per vehicle by the number of vehicles that switch to natural gas due to the project, and the number of miles traveled per vehicle per year:
Table 82. Sample Calculation of Annual Emission Benefit of a
| Pollutant | Emission Impact per Bus (grams/mile) | Annual VMT per Bus | Number of Trucks Affected per Year | Annual Emission Benefit (grams) | Annual Emission Benefit (tons) |
|---|---|---|---|---|---|
| PM2.5 | 0.027 | 45,000 |
30 |
36,450 | 0.0402 |
| PM10 | 0.028 | 37,800 | 0.0417 |
Step Two: The estimated annual emission impact is multiplied by the project lifetime to identify project-level emission impacts:
Table 83. Sample Calculation of Annual Emission Benefit of a
| Pollutant | Annual Emission Benefit (tons) | Project Lifetime (years) | Lifetime Emission Benefit (tons) |
|---|---|---|---|
| PM2.5 | 0.0402 | 20 |
0.8036 |
| PM10 | 0.0417 | 0.8333 |
Step Three: The project cost is divided by the estimated project-level emission impact to yield the cost-effectiveness estimate:
Table 84. Sample Calculation of Cost-Effectiveness Estimate for a
| Pollutant | Lifetime Emission Benefit (tons) | Project Cost | Cost-Effectiveness (dollars per ton) |
|---|---|---|---|
| PM2.5 | 0.8036 | $20,000,000 |
$24,888,477 |
| PM10 | 0.8333 | $23,999,603 |
The median cost-effectiveness estimates for the range of scenarios are presented in Table 85 below:
Table 85. Median Cost-Effectiveness Estimates (Dollars per Ton of PM10)
| Pollutant | Cost-Effectiveness (dollars per ton) |
|---|---|
| PM2.5 | $4,507,710 |
| PM10 | $4,269,635 |
This section reviews the analysis of ridesharing projects. Ridesharing projects center on the support of programs designed to encourage mode shift from single-occupant light-duty vehicle to multiple-occupant vehicles (carpools and vanpools). Ridesharing projects may involve direct subsidies of drivers of shared vehicles, the purchase of vanpools, and indirect support such as ride-matching services.
In the analyses of ridesharing projects, key inputs included:
The steps required to conduct the analysis of ridesharing projects include:
As an illustrative example consider a scenario involving a vanpool program, designed to encourage drivers of single-occupant vehicles to reduce drive-alone trips to and from work.
In this scenario, we assume the following details:
Step One: Annual emission benefits are identified by multiplying per-trip single-occupant vehicle emissions by the number of mitigated trips:
Step Two: Annual emission impacts are identified by subtracting new van emissions from the annual emission benefit identified in Step One:
Step Three: Each of the estimated annual emission impacts is multiplied by the project lifetime to identify project-level emission impacts:
| Pollutant | Annual Net Emission Impact (tons) | Project Lifetime (years) | Lifetime Emission Impact (tons) |
|---|---|---|---|
| NOx | 0.1929 | 5 |
0.9645 |
| PM2.5 | 0.0061 | 0.0303 |
| Pollutant | Lifetime Emission Impact (tons) | Project Cost | Cost-Effectiveness ($/ton) |
|---|---|---|---|
| CO | 0.9645 | $600,000 |
$622,070 |
| NOx | 0.0303 | $19,793,127 |
Step Four: The project cost is divided by the estimated project-level emission impacts to yield cost-effectiveness estimates:
| Pollutant | Lifetime Emission Impact (tons) | Project Cost | Cost-Effectiveness ($/ton) |
|---|---|---|---|
| CO | 0.9645 | $600,000 |
$622,070 |
| NOx | 0.0303 | $19,793,127 |
The median cost-effectiveness estimates for the range of scenarios are presented in Table 90 below:
