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Renewable portfolio standards (RPSs׳) require a certain fraction of the electricity generated for a given region be produced from renewable resources. California׳s RPS mandates that by 2020, 33% of the electricity sold in the state must be generated from renewables. Such mandates have important implications for the electricity sector as well as for the whole society. In this paper, we estimate the costs and benefits of varying 2020 California RPS targets on electricity prices, greenhouse gas (GHG) emissions, criteria pollutant emissions, the electricity generation mix, the labor market, renewable investment decisions, and social welfare. We have extended the RPS Calculator model, developed by Energy and Environmental Economics (E3) Inc., to account for distributions of fuel and generation costs, to incorporate demand functions, and to estimate the effects of RPS targets on GHG emissions, criteria pollutant emissions, and employment. The results of our modeling provide the following policy insights: (1) the average 2020 electricity price increases as the RPS target rises, with values ranging between $0.152 and $0.175/kW h (2008 dollars) for the 20% RPS to 50% RPS, respectively; (2) the 33% and 50% RPS targets decrease the GHG emissions by about 17.6 and 35.8 million metric tons of carbon dioxide equivalent (MMTCO 2 e) relative to the 20% RPS; (3) the GHG emission reduction costs of the RPS options are high ($71–$94 per ton) relative to results from policy options other than RPS or prices that are common in the carbon markets; and (4) a lower target (e.g., a 27% RPS) provides higher social welfare than the 33% RPS (mandate) under low and moderate CO 2 social costs (lower than $35/ton); while a higher RPS target (e.g., 50%) is more beneficial when using high CO 2 social costs or rapid renewable technology diffusion. However, under all studied scenarios, the mandated 33% RPS for California would not provide the best cost/benefit values among the possible targets and would not maximize the net social benefit objective. [ABSTRACT FROM AUTHOR]

The application of public–private partnerships (P3’s) in the transportation sector has grown in popularity worldwide. Despite this important shift in the provision of transportation service, there are clear gaps in knowledge about the impacts of P3 projects, especially on emissions from transportation systems as a whole. Not only should policy makers evaluate the emissions impacts from P3 projects, but they should also think about innovative models that address or charge for emissions into P3 contracts. This addition to P3 contracts could provide a new solution to the long-existing property right paradox: who owns (is responsible for) emissions from transportation systems? This study attempts to fill the research gap by analyzing these innovative models. Using the road network of Fresno, California, as our case study, we offer a number of interesting insights for policy makers. First, average peak emissions costs range from 1.37 cents per mile (the do-nothing case) to 1.20 cents per mile (profit-maximizing cases) per vehicle. Although emissions costs from the P3 projects are lowest for the profit-maximizing cases, the system-wide emissions costs of these cases are highest because of spillover effects. Second, charging project owners for the emissions costs of P3 projects is not an effective way to reduce emissions or the total costs of travel, especially on a VMT basis. Instead, the public sector should implement emissions-included social cost-based price ceilings. When employing these limits, project owners could still be charged for the emissions costs. Finally, using total travel time as the only objective function for evaluating P3 projects can be misleading. Several P3 projects have shown better outcomes using total travel cost with the inclusion of emissions and fuel consumption costs, instead of using total travel time as the only objective function. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper examines the effects of spatially-variable pricing along road corridors. [•] Applying spatially-variable tolls could effectively increase system performance and profits. [•] Volumes over capacity (V/Cs) on different segments are crucial factors. [•] Target V/Cs – rather than VMTs, MCs, or elasticities – determine the optimal tolls. [•] The target V/C patterns are similar for peak and off-peak, even using different toll rates. [Copyright &y& Elsevier]

Abstract: Increasing private sector involvement in transportation services has significant implications for the management of road networks. This paper examines a concession model’s effects on a road network in the mid-sized city of Fresno, California. Using the existing transportation planning models of Fresno, we examine the effects of privatization on a number of typical system performance measures including total travel time and vehicle miles traveled (VMT), the possibility of including arterials, and the differences between social cost prices and profit maximizing prices. Some interesting insights emerge from our analysis: (1) roads cannot be considered as isolated elements in a concession model for a road network; (2) roads can function as complements at some levels of demand and become substitutes at other levels; (3) policy makers/officials should consider privatizing/pricing arterials along with privatizing highways; (4) temporally flexible but limited price schedule regulations should be part of leasing agreements; and (5) non-restricted pricing may actually worsen system performance, while limited pricing can raise enormous profits as well as improve system performance. [Copyright &y& Elsevier]

Abstract: Ozone pollution is caused by photochemical reactions of precursor volatile organic compounds and nitrogen oxides, of which transportation emissions are the single major source. Understanding the complexity of diurnal ozone processes and their relationships with precursors is thus critical for identifying optimal transportation emissions control strategies. This paper uses functional data analysis methods to model the dynamics of diurnal ozone and nitrogen oxides cycles, their interrelationships, and the multilevel spatio-temporal variations of ozone and nitrogen oxides measurements from Southern California. Functional data analysis accounts for the continuous nature of diurnal ozone/nitrogen oxides processes by converting discrete observations into functional diurnal curves. Results show important, but low-dimensional, spatio-temporal variations in diurnal ozone/nitrogen oxides cycles. Representative summer diurnal ozone profiles are constructed using functional clustering. Variability in hourly distribution of traffic activities and emissions is also discussed. [Copyright &y& Elsevier]

Abstract: The ozone weekend effect refers to the counterintuitive observations showing weekend ozone concentrations frequently to be higher than or comparable to those observed on weekdays. Ozone dynamics are closely linked to the timing, magnitude and fleet mix of transportation activities, primary sources of ozone precursor emissions. To examine the effects of traffic activity on the ozone weekend effect, a statistical analysis was conducted of the weekly patterns of time dependent light-duty vehicle and heavy-duty truck volumes observed at 27 weigh-in-motion stations in southern California. The results show statistically significant variations in traffic flows by day of week, by vehicle type, and by location with respect to the Los Angeles metropolitan area. These variations in traffic, when converted to variations in running exhaust emissions, tend to support four of the seven California Air Resources Board’s ozone weekend effect hypotheses. [Copyright &y& Elsevier]

Abstract: Since September 11, 2001, airport screening procedures in the US have been continuously evolving. For example, the passenger screening process is now trying to strike a balance between security and customer service (i.e. minimizing wait times). This balancing act has important implications not only for passenger safety, but also for the financial stability of an airline industry that is faced with volatile energy prices and sometimes burdensome labor agreements. Using data from 2002 and 2003, we estimate multinomial logit models to uncover factors that determine passenger satisfaction at security screening points. Our findings show that, while wait times at security screening points are significant determinants of passenger satisfaction, many other factors come into play. Moreover, the results show that the determinants of customer satisfaction are not stable over time. This suggests that further refinements in airport screening procedures should give careful consideration to the factors underlying passenger satisfaction, and how these might change over time, rather than focusing exclusively on minimizing wait times at passenger screening points. [Copyright &y& Elsevier]

Abstract: This study explores the differences between urban and rural driver injuries (both passenger-vehicle and large-truck driver injuries) in accidents that involve large trucks (in excess of 10,000 pounds). Using 4 years of California accident data, and considering four driver-injury severity categories (no injury, complaint of pain, visible injury, and severe/fatal injury), a multinomial logit analysis of the data was conducted. Significant differences with respect to various risk factors including driver, vehicle, environmental, road geometry and traffic characteristics were found to exist between urban and rural models. For example, in rural accidents involving tractor–trailer combinations, the probability of drivers’ injuries being severe/fatal increased about 26% relative to accidents involving single-unit trucks. In urban areas, this same probability increased nearly 700%. In accidents where alcohol or drug use was identified as being the primary cause of the accident, the probability of severe/fatal injury increased roughly 250% percent in rural areas and nearly 800% in urban areas. While many of the same variables were found to be significant in both rural and urban models (although often with quite different impact), there were 13 variables that significantly influenced driver-injury severity in rural but not urban areas, and 17 variables that significantly influenced driver-injury severity in urban but not rural areas. We speculate that the significant differences between rural and urban injury severities may be at least partially attributable to the different perceptual, cognitive and response demands placed on drivers in rural versus urban areas. [Copyright &y& Elsevier]

Abstract: An exploratory simulation model is developed to examine the effects of land-use policies on transportation activities and vehicle emission. It is assumed individuals choose to live among 3 zones in each of two neighboring cities and commute to work by auto. Residential choice is assumed to be the result of optimizing transportation and housing costs. Vehicle emissions are modeled as a function of each city’s population, trip length and route capacity. Three scenarios are constructed to investigate the combined effects of land-use policies and other common polices such as rent control and control of land supply measures on changes in vehicle emissions. The results indicate that mixing residential and business uses in one city may result in residential price increases, and thus displace incumbent residents to a neighboring city. The displacement may create a net increase in vehicle emissions if the new route to work is more congestion-prone and has lower capacity than the old route. Displacement, and hence vehicle emissions tend to increase when the price responsiveness of residential demands in both neighboring cities are comparable. We also examine the impacts from two other common polices such as rent subsidies programs and control of land supply. It is found that these policies, if implemented together with land-use polices may exacerbate the displacement of residents to other housing markets. In light of the results, it is important for the policy makers to take into account of residential mobility in evaluating the effectiveness of any land-use policy aimed at reducing vehicle emissions. [Copyright &y& Elsevier]

Abstract: In air quality modeling, vehicle survival rates, also frequently referred to as retention rates, are very important for modeling mobile emissions inventories. The vehicle survival rate determines how many vehicles in the current fleet will survive to a future year, which, when added to new vehicle sales, provides a vehicle population forecast for a given year. In this study, we specify a mass point duration model that agencies like the California Air Resources Board (CARB) and the Environmental Protection Agency (EPA) can use, which will significantly improve the current practice for estimating vehicle survival rates. The data used for the model represents snapshots of passenger car smog check data collected between 1998 and 2002. We use a stratified sampling procedure to select a sample of 678 observations from the original vehicle population of 2.2 million for model specification. Using the sample, we applied a duration model of the Weibull form with two mass points to approximate the unobserved heterogeneity to estimate vehicle survival rates. The results of our vehicle scrappage model are consistent with evidence from the literature suggesting that vehicle age is an important variable influencing the scrappage decision. However, our study also shows that there are other variables that appear equally influential. To compare our model results with the CARB model, we also calculated the survival function and compared the average survival probability by age group to the survival rates used by CARB. From this, we observe that incorporating other variables in addition to vehicle age generally suggests a higher survival rate for the same age group compared to the survival rates used by CARB. [Copyright &y& Elsevier]

Current modeling methods are inadequate for producing gridded mobile source inventories suitable for simulating fine-scale changes in emissions for transportation planning or regulatory control purposes. In this paper, we present a new model that more precisely simulates small-scale emission changes that can arise from, for example, implementation of traffic control measures in transportation networks. The new model, UCDrive, directly interfaces with most travel demand models and with California''s regulatory emission factor model EMFAC2001/2002. The model improves the sensitivity of emission estimates to variations in vehicle speeds and uses a theoretically consistent approach to spatially disaggregating mobile emissions. To demonstrate application of the model we use the Sacramento area network to perform a comparison study. We show that regional emissions with the new model replicate regulatory inventories, which is critical for using the model for regulatory purposes. We also show that the new model''s results better replicate regulatory totals than those derived with a frequently used gridded emissions inventory model. The new model should improve the ability to better simulate changes in mobile emissions from transportation projects and help to bridge the gap between gridded mobile emission inventories and regional mobile emission inventories. [Copyright &y& Elsevier]

As the fundamental building block of the emissions estimation process, a driving cycle needs to be representative of real-world driving behavior. The driving cycle construction method becomes crucial for generating a representative driving cycle. In this paper, we revisit the Unified Cycle''s (i.e., the LA92 driving cycle) construction method. The California Air Resources Board''s Unified Cycle used a “microtrips” approach, a speed–acceleration frequency distribution plot, and a quasi-random selection mechanism to build the driving cycle. There is concern that the Unified Cycle does not reflect the true driving patterns due to the identified flaws in the construction methodology. Treating a driving trace as a stochastic process, we construct a new driving cycle (LA01) with the same driving data originally used to build the Unified Cycle. We then compare the two driving cycles with the sample data set with respect to the durations and intensities of the modal events. The new driving cycle is found to better replicate the modal events observed in the sample data. A comparison of average road power values between the sample data, LA01, and the Unified Cycle also confirms the effect of fine-scale driving on emissions. These differences result from the different construction approaches and can be expected to affect emissions inventory estimation. [Copyright &y& Elsevier]

This paper explores the range of CALINE4''s PM2.5 modeling capabilities by comparing previously collected PM2.5 data with CALINE4 predicted values. Two sampling sites, a suburban site located at an intersection in Sacramento, CA, and an urban site located in London, were used. Predicted concentrations are graphed against observed concentrations and evaluated against the criterion that 75% of the points fall within the factor-of-two prediction envelope. For the suburban site, data estimated by CALINE4 produced results that fell within the acceptable factor-of-two percentage envelope. A reverse dispersion test was also conducted for the suburban site using observed and calculated emission factors, and although it showed correlations between the observed values and CALINE4 predicted values, it could not conclusively prove that the model is accurate at predicting PM2.5 concentrations. Although the results suggest that CALINE4 PM2.5 predictions may be reasonably close to observed values, the number of observations used to verify the model was small and consequently, findings from the suburban site should be considered exploratory. For the urban site, a much larger data set was evaluated; however, the CALINE4 results for this site did not fall 75% within the factor-of-two envelope. Several factors, including street canyon effects, likely contributed to an inaccuracy of the emission factors used in CALINE4, and therefore, to the overall CALINE4 predictions. In summary, CALINE4 does not appear to perform well in densely populated areas and differences in topography may be a decisive factor in determining when CALINE4 may be applicable to modeling PM2.5. For critical transportation projects requiring PM2.5 analysis, use of CALINE4 may not be optimal because of its inability to produce reasonable estimates for highly trafficked areas. Additional data sets for CALINE4 analysis, particularly in urban environments, are required to fully understand CALINE4''s PM2.5 modeling capabilities. [Copyright &y& Elsevier]

The current method used for calculating vehicle emissions integrates travel data and associated vehicle emission factors. Travel data from traditional travel demand models are normally link-based (e.g., volumes on roadway segments), while California emission factors are trip-based (i.e., average emission factors over an entire trip), creating a mismatch in the modeling interface. Using dynamic simulation for trip assignment, we present a new modeling framework that consistently provides both trip-based and link-based VMT-speed distributions. Using the Sacramento Metropolitan Area and Kern County in California, we demonstrate the feasibility of this new method and quantify the effects of using trip-based versus link-based travel data on regional peak period emission inventories. The comparison results indicate that for the base scenario in both studied regions, the link-based method generally results in higher emissions than the trip-based method. The sensitivities of the link and trip-based methods to road network variations also appear dissimilar. The link-based emissions are more sensitive to facility-related changes, while the trip-based emissions are more sensitive to demand-related changes. This suggests that greater care may need to be taken to specify the effects of this modeling interface issue within the transportation conformity process and subsequent mobile emissions analysis. [Copyright &y& Elsevier]

Abstract: Average roadway segment travel speeds play an important role in estimating stabilized running vehicle emissions. Currently stabilized, or hot, running emissions are computed based on speeds produced during the travel demand modeling process. Speed data from the travel forecasting models are widely recognized as being insufficiently accurate for air quality purposes. Frequently post-processing techniques are seen as the most cost-effective means of improving the accuracy of the speed estimates. Using the Sacramento Metropolitan area, this paper focuses on the impacts of different speed post-processors on regional peak period emissions inventories. The results indicated that most post-processed speeds produce consistently and significantly higher running emissions, particularly in locations with heavy traffic. The observed differences in emissions between different types of post-processed speeds vary with congestion level, pollutant type and the underlying approach encapsulated in the speed post-processor calculations. The Sacramento case study suggests that the post-processor used to develop speeds for the purposes of calculating on-road emissions inventories can significantly influence the emissions inventories. [Copyright &y& Elsevier]

Abstract: On-road size-resolved particulate emission factors were computed using concurrently measured carbon monoxide (CO) as a freeway dilution indicator and correlating roadside particle measurements to CO measurements. The emission factors derived for the total particle number agree well with previous on-road investigations. However, this study extends this analysis to produce unique receptor-dependent, size-resolved, road and grid-level emission factor distributions. Both mileage- and fuel-based particle number and mass emission factors at road and grid levels, along with CO emission factors, are presented and the results from freeways with distinctly different percentages of heavy-duty diesel truck traffic are compared. The effects of plume processing on particle number near roadways are shown to be much more profound than on particle mass, further indicting that the adverse health effects observed near roadways are at least partially related to particle numbers. [Copyright &y& Elsevier]

Abstract: The Ozone Weekend Effect (OWE) has become increasingly more frequent and widespread in southern California since the mid-1970s. Although a number of hypotheses have been suggested to explain the effect, there remains uncertainty associated with the root factors contributing to elevated weekend ozone concentrations. Targeting the time window of the 1997 Southern California Ozone Study (SCOS97), this paper examines traffic activity data for 14 vehicle classes at 27 weigh-in-motion (WIM) stations in southern California. Nonparametric factorial analyses of light-duty vehicle (LDV) and heavy-duty truck (HDT) traffic volumes indicate significant differences in daily volumes by day of week and between the weekly patterns of daily LDV and HDT volumes. Across WIM stations, the daily LDV volume was highest on Friday and decreased by 10% on weekends compared to that on midweek days. In contrast, daily HDT volumes showed dramatic weekend drops of 53% on Saturday and 64% on Sunday. As a result, LDV to HDT ratios increased by 145% on weekends. Nonparametric tests also suggest that weekly traffic patterns varied significantly between WIM stations located close to (central) and far from (peripheral) the Los Angeles Metro area. Weekend increases in LDV/HDT ratios were more pronounced at central WIM sites due to greater weekend declines of HDT relative to LDV traffic. The implications of these weekly traffic patterns for the OWE in southern California were investigated by estimating daily WIM traffic on-road running exhaust emissions of total organic gas (TOG) and oxides of nitrogen (NO x ) using EMFAC2002 emission factors. The results support the California Air Resource Board''s (CARB''s) NO x reduction hypothesis that greater weekend NO x reductions relative to volatile organic compound (VOC) emissions, in combinations with the VOC-limited ozone system, contribute to the OWE observed in the region. The results from this study can be used to develop weekend on-road mobile emission inventories for the purpose of air quality modeling. [Copyright &y& Elsevier]

An improved dispersion model, UCD 2001, designed to estimate pollutant concentrations near roadways was developed and its performance evaluated. The UCD 2001 model internally represents a highway link as a three-dimensional array of point sources that simulates a roadway mixing zone which extends 2.5 m above a highway link. Dispersion from each point source is estimated with the Huang dispersion solution. The Huang equation is a simplified solution to the semi-empirical advection diffusion equation; its derivation permits vertical profiles of wind speed and eddy diffusivity in the boundary layer to be approximated by power law functions.The UCD 2001 model was calibrated with one-half of the General Motors (GM) SF6 tracer study data base and resulted in a selection of eddy diffusivity parameters that did not vary with ambient meteorology. This parameterization is consistent with several independent studies which indicate that the atmosphere is well-mixed and neutrally stratified immediately downwind of a roadway with significant vehicular activity.UCD 2001 model performance was evaluated and compared to the CALINE3 and CALINE4 dispersion models using the GM data base. UCD 2001 adequately simulates near parallel, low wind speed (less than 0.5 m/s) meteorological scenarios, whereas the CALINE models significantly over predict most receptor concentrations for these conditions. The UCD 2001 model results in approximately 80–90 percent reduction in squared residual error when compared to the CALINE3 and CALINE4 models. In addition, the UCD 2001 model exhibits better agreement in simulating the top forty observed concentrations than either CALINE model. Lastly, the UCD 2001 model requires less user input and modeler expertise than most roadway dispersion models, and should result in more consistent and robust pollutant field estimations. [Copyright &y& Elsevier]

Dockless bikeshare systems show potential for replacing traditional dock-based systems, primarily by offering greater flexibility for bike returns. However, many cities in the US currently regulate the maximum number of bikes a dockless system can deploy due to bicycle management issues. Despite inventory management challenges, dockless systems offer two main advantages over dock-based systems: a lower (sometimes zero) membership fee, and being free-range (or, at least free-range within designated service areas). Moreover, these two advantages may help to solve existing access barriers for disadvantaged populations. To date, much of the research on micro-mobility options has focused on addressing equity issues in dock-based systems. We have limited knowledge of the extent to which dockless systems can help mitigate barriers to bikeshare for disadvantaged populations. Using San Francisco as a case study, because the city has both dock-based and dockless systems running concurrently, we quantify bikeshare service levels for communities of concern (CoCs) by analyzing the spatial distribution of service areas, available bikes and bike idle times, trip data, and rebalancing among dock-based and dockless systems. We find that dockless systems can provide greater availability of bikes for CoCs than for other communities, attracting more trip demand in these communities because of a larger service area and frequent bike rebalancing practices. More importantly, we notice that the existence of electric bikes helps mitigate the bikeshare usage gap between CoCs and other tracts. Our results provide policy insights to local municipalities on how to properly regulate dockless bikeshare systems to improve equity. [ABSTRACT FROM AUTHOR]

Abstract: Future air pollution emissions in the year 2030 were estimated for the San Joaquin Valley (SJV) in central California using a combined system of land use, mobile, off-road, stationary, area, and biogenic emissions models. Four scenarios were developed that use different assumptions about the density of development and level of investment in transportation infrastructure to accommodate the expected doubling of the SJV population in the next 20 years. Scenario 1 reflects current land-use patterns and infrastructure while scenario 2 encouraged compact urban footprints including redevelopment of existing urban centers and investments in transit. Scenario 3 allowed sprawling development in the SJV with reduced population density in existing urban centers and construction of all planned freeways. Scenario 4 followed currently adopted land use and transportation plans for the SJV. The air quality resulting from these urban development scenarios was evaluated using meteorology from a winter stagnation event that occurred on December 15th, 2000 to January 7th 2001. Predicted base-case PM2.5 mass concentrations within the region exceeded 35 μg m−3 over the 22-day episode. Compact growth reduced the PM2.5 concentrations by ∼1 μg m−3 relative to the base-case over most of the SJV with the exception of increases (∼1 μg m−3) in urban centers driven by increased concentrations of elemental carbon (EC) and organic carbon (OC). Low-density development increased the PM2.5 concentrations by 1–4 μg m−3 over most of the region, with decreases (0.5–2 μg m−3) around urban areas. Population-weighted average PM2.5 concentrations were very similar for all development scenarios ranging between 16 and 17.4 μg m−3. Exposure to primary PM components such as EC and OC increased 10–15% for high density development scenarios and decreased by 11–19% for low-density scenarios. Patterns for secondary PM components such as nitrate and ammonium ion were almost exactly reversed, with a 10% increase under low-density development and a 5% decrease under high density development. The increased human exposure to primary pollutants such as EC and OC could be predicted using a simplified analysis of population-weighted primary emissions. Regional planning agencies should develop thresholds of population-weighted primary emissions exposure to guide the development of growth plans. This metric will allow them to actively reduce the potential negative impacts of compact growth while preserving the benefits. [Copyright &y& Elsevier]

For air quality dispersion models, mobile source emissions, including both link- and traffic zone-level emissions, must be disaggregated into grid cells. Current gridding methods assign all traffic analysis zone level emissions to the single grid cell containing the TAZ centroid. In this study, we propose a new approach for disaggregating traffic analysis zone-level emissions using a bicubic spline interpolation function and activity and roadway densities. The new approach, which better replicates the heterogeneity associated with travel activities, distributes zone-level emissions into the grid cells contained within the zone boundary. When results are compared to the current methods, we find that fewer grid cell misallocations occur and that emissions from TAZs overlapping multiple grid cells are apportioned correctly. The gridded emissions inventory developed using the new approach will result in better data inputs for air quality modeling, and in particular can significantly improve the sensitivity of transportation conformity analysis. [Copyright &y& Elsevier]