The Impacts of Land Use and Pricing in Reducing Vehicle Miles Traveled and Transport Emissions in Massachusetts
A research brief from the Metropolitan Area Planning Council
Published January 22, 2021
Conor Gately, Senior Land Use and Transportation Analyst
Tim Reardon, Data Services Director
In order to achieve Massachusetts’ ambitious greenhouse gas reduction targets, the state will need to improve substantially the fuel efficiency of cars and trucks and to pursue a complete transition to renewably-powered electric vehicles. Efficiency is only part of the solution, though. It also matters how much people are driving. All other things being equal, more vehicle miles traveled (VMT) necessarily means more emissions and greater demand for electricity.
Both land use and the cost of driving are important factors in how much people choose to drive. When development is spread across the region, people need to travel farther to get to work or run errands, and it is less likely that they will be able to take transit for their trips. When gas prices and gas taxes are low and use of the roadway is essentially free, then drivers are less likely to opt for transit even when it is available. Regardless of how travel behavior changes post-COVID, these basic patterns and preferences are likely to persist.
Using a pair of established land use and travel behavior models, MAPC has forecast growth in VMT by 2030 under various scenarios to estimate the relative impact of different land use patterns and pricing policies, alone and in combination with each other. Three land use scenarios represented different patterns of housing growth in the region, ranging from urban-focused to dispersed across many different types of communities. The pricing scenarios included gas tax increases, a VMT fee, and congestion pricing.
- Absent any major changes in land use or roadway pricing, VMT for personal vehicles is projected to increase by more than 21% between 2010 and 2030. Under such circumstances, fleetwide average fuel efficiency would need to improve to 29 miles per gallon by 2030 just to keep transportation GHG emissions from growing. Even a return to the Obama-era fuel efficiency standards would be insufficient to achieve this target given current rates of vehicle turnover.
- Land use patterns have a substantial impact on the rate of VMT increase: VMT growth in a “sprawl” scenario is 5.2 percentage points higher than a “smart growth” scenario with more development in urban areas and denser suburbs. These conservative estimates don’t yet account for the benefits of transit improvements or transit-oriented development, factors that will be addressed in the next phase of research.
- Of the pricing policies tested, a straight 25-cent per mile fee had the largest impact, curbing VMT growth by about 15 percentage points. Congestion pricing, broadly applied, would have a slightly smaller effect. Our results also suggest that even a tripling of the gas tax would slow VMT growth by only one or two percentage points—a much smaller effect than the smart growth land use patterns we examined.
These results indicate the Commonwealth cannot rely solely on technology-based solutions such as fuel efficiency and electrification to meet its ambitious GHG reduction targets. If VMT growth continues unabated, vehicle emissions could rise even as fuel economy improves, and rapidly increasing vehicle demand for renewable electricity will make it harder to transition other sectors to renewables. A reduction in VMT has other benefits as well: less congestion, more livable communities, and safer streets. Smart growth land use policies and user fees must be part of any comprehensive transportation sustainability strategy, but as we implement such policies, we must make sure they are designed and applied in ways that advance racial and economic equity. We must ensure that new user fees account for the income or assets of the payer. We must ensure access to fair and affordable housing in every community, especially near jobs, transit, and strong schools as a way to advance economic opportunity.
MAPC pledges to continue its analysis into these issues to support effective state and local planning for mobility, climate, and equity.
Massachusetts has set ambitious goals for reducing greenhouse gas emissions and is currently developing updated plans to meet targeted reductions for 2030 and 2050. The transportation sector is the largest source of greenhouse gas (GHG) emissions in the Commonwealth; as such, it must achieve substantial reductions in emissions. Most transportation emission reduction strategies focus on technological and infrastructure solutions that will reduce the emissions per mile traveled: improved fuel efficiency of the internal combustion vehicle fleet, more widespread adoption of electric vehicles, and eventual transition to a fully electric fleet powered by renewable energy. Implementation of this pathway could yield dramatic reductions in emissions, but it will take time to transition the fleet and develop the scale of renewable resources to power all the state’s personal travel demand.
Complete decarbonization of the transportation system requires time and renewable energy infrastructure, so a comprehensive climate plan cannot rely solely on efficiency and electrification if the amount of driving keeps rising unsustainably. Reduction of VMT must also be a key climate policy strategy, for a variety of reasons: continued growth in VMT will counteract and delay the benefits of efficiency improvements; more driving means expansion of carbon-intensive auto infrastructure and associated heat islands from additional impervious pavement; and auto-dominated landscapes diminish pedestrian safety and discourage active transportation. Curbing all these effects will require policies, practices, and behaviors that reduce trip lengths and shift more travel to transit, walking, biking, or other low-carbon modes. Infrastructure, land use, and pricing are all key factors for trip making and mode choice. In order to demonstrate the relevance of such strategies, MAPC analyzed the impacts of broad-scale land use changes and potential gas tax and roadway user fees in Metro Boston.
MAPC modeled a range of VMT forecasts for the region, and found that, absent any major changes in land use or pricing policies, VMT for personal vehicles is projected to increase by more than 21% between 2010 and 2030, consistent with observed trends since 2010. In such a future, vehicle fuel efficiency would need to improve by at least 18% (to a fleet-wide average of 29 miles per gallon) before any net reductions in GHG are achieved. These estimates do not account for any long-term travel changes that may result from the COVID pandemic, the effects of which will not be fully known for many years. However, they provide a base-case scenario against which post-pandemic travel assumptions could be tested in future research efforts.
The base-case scenario was compared with a variety of alternative land use and pricing scenarios in order to assess the relative impact of various land use patterns, fuel taxes, and user fees. As described in subsequent sections, MAPC developed three land use scenarios and tested various combinations of taxes and fees. The initial results described here show that broad scale changes in land use patterns can have a substantial effect on forecast growth of VMT. User fees such as a VMT fee or congestion fee have an even larger effect. Patterns in land use can compound the effectiveness of all the pricing strategies. Our results also suggest that even a tripling of the gas tax would have little impact on the amount of driving.
More work is needed to fully understand the complex interplay of factors affecting VMT. For example, none of the scenarios yet account for the benefits of improved transit service, transit-oriented development, safer active transportation connections, or expanded telework and delivery. More work is also needed to define how such scenarios could be achieved equitably, ensuring fair and affordable housing opportunities throughout the region and mitigating transportation cost burden on low-income households. Nevertheless, these results demonstrate that it is essential to pursue VMT reduction through a variety of means if technological improvements are to make headway on achieving ambitious GHG emissions reduction goals.
This report summarizes MAPC’s forecasts of VMT growth from 2010 to 2030 under a “business as usual” scenario, and the relative reduction potential of a suite of land use and transportation pricing policies. Central to this analysis are a complementary pair of land use and transportation models - UrbanSim and VisionEval - which have been developed and deployed in a growing number of other metro areas around the United States. MAPC has spent the last year working to calibrate and tailor these models to the MAPC region. We use UrbanSim to create alternative land use scenarios for the region, each representing a different distribution of future growth across the region. The resulting outputs are then fed into VisionEval, which estimates travel demand and VMT for each land use scenario based on specified assumptions about the cost of gas and driving. The two models are described in more detail below.
UrbanSim is a microsimulation model that allocates forecasted population, households, and employment growth in the MAPC region to individual census blocks based on household/employer preferences, existing land use, development potential, and other factors. Core inputs include land use controls (zoning), planned development, and travel times and costs (from the region’s travel demand model). The model allows MAPC to explore a range of future scenarios for population and housing growth, simulating different patterns of land use. Using this tool, we produced three different land use scenarios, each representing distinct land use growth patterns across the region. Each scenario had the same amount of population, household, and employment growth. They varied in the spatial distribution of an estimated 224,000 new jobs and 318,000 new housing units needed to accommodate projected population and household growth over the 20-year forecast period.
Co-Benefits of Reducing VMT
In addition to reducing carbon emissions, it is worthwhile to recognize the additional benefits that efforts to reduce VMT can yield:
- Reducing VMT will help alleviate traffic congestion, which was at unprecedented levels prior to the COVID-19 pandemic with drivers spending 190,000 person-hours per year in traffic in the Boston metro area alone.
- Improving access to jobs and services for residents without a personal vehicle will help bolster local economic development efforts at a time when they are most needed.
- Reduction of VMT will also enable communities to reclaim streets and parking lots currently used for travel or parking, creating more pedestrian-friendly and economically vibrant places.
- Auto travel also causes 360 deaths annually in Massachusetts due to crashes, and a reduction in VMT will help to reduce the risk to both drivers and pedestrians.
Investments in public transit are likely to be more effective job creators than roadway expansion projects. A study evaluating the job creation impacts of the American Reinvestment and Recovery Act (ARRA) in 2009 found that public transportation investments generate 31% more jobs per dollar than construction of new roads and bridges
VisionEval1 is a generalized transportation model that estimates the impact of different land use and pricing policies on vehicle travel. It does this by modeling the travel behavior of households along with their travel expenditures. Using data from the National Household Travel Survey and the Consumer Expenditure Survey, a suite of statistical models within VisionEval predict household vehicle ownership, household trips per day by mode, and the share of a household’s income available to be spent on travel, as a function of household and neighborhood characteristics. The model generates a “travel budget” for each household and compares it with the cost of trips available to the household via different modes (personal vehicle, ride-share/car service, transit, bike/walk). As the price of different modes changes, estimated household trips are re-allocated to satisfy the budget requirement. When the budget is spent, no additional trips (aside from bike and walk) are allocated to the household. Thus, as the price per mile of a vehicle trip increases (due to changes in the price of fuel, VMT fees, or congestion fees), the length and/or number of household vehicle trips is reduced. The relative impact of different pricing policies depends on the characteristics of each household and of the trips that it takes.
VisionEval allows the user to evaluate a wide range of transportation policies, comparing their impacts on vehicle miles traveled and related greenhouse gas emissions. Model parameters include the future price of motor gasoline, the state tax on gasoline, per-mile taxes on VMT, congestion fees on freeways or arterial roads, changes in regional transit accessibility, and the pricing of parking in different model zones. Outputs of the model include average household-level VMT, transportation expenditures, vehicle ownership rates, TNC usage, vehicle CO2 emissions, and other indicators. Although not as comprehensive or sophisticated as a travel demand model that forecasts individual trip making behavior, the reduced complexity of VisionEval allows for more rapid evaluation of multiple scenarios. In this study, we focus on exploring the three land use scenarios in combination with the main pricing variables in the VisionEval model: the value of the gasoline tax, a per-mile tax on vehicle miles traveled, and a per-mile congestion fee applied to travel on highways.
Land Use Scenarios
To create the alternative land use scenarios, MAPC specified the share of new household growth in each of four basic Community Types in the region: the high-density, transit rich Inner Core municipalities; outlying Regional Urban Centers (generally served by commuter rail); moderate density Maturing Suburbs; and lower-density Developing Suburbs. Municipalities within each of these community types share similar characteristics with regard to demographics, housing stock, existing land use, vehicle ownership, and household VMT.
The base scenario, characterized as “Trends of the 2000s,” represents future development patterns consistent with the 2000-2010 growth patterns across the region. The “Sprawl” scenario assumes resurgence of suburban housing development based on patterns of growth in the 1970s. The “Smart Growth” scenario is characterized by population growth focused on Inner Core municipalities, with considerably less growth in suburban areas. Employment growth by community type remained constant in each scenario though the distribution within community types varies since it is influenced by household allocation.
The graph in Figure 1 and maps in Figure 2 show the different spatial patterns of population growth between 2010 and 2030 for the three land use scenarios. It should be noted that none of the scenarios target growth within each Community Type to specific transit-rich municipalities or transportation efficient locations (such as transit areas or town centers.) As a result, the differences between land use scenarios may be less distinct than would be created by more targeted assumptions and should be considered conservative estimates of the difference between scenarios. Also, since the UrbanSim model is still being refined and calibrated, these scenarios are intended to be illustrative; they are not intended to represent definitive forecasts for individual cities, towns, and neighborhoods. Our next phase of research will entail improvements to the model’s zoning and development inputs; and development of nuanced land use scenarios that assess the impact of land use policies that target growth to specific transportation efficient locations across the region and within municipalities.
Figure 1. Comparison of share of household growth by MAPC Community Type for the “Trends of the 2000s”, “Smart Growth”, and “Sprawl” land use scenarios.
Figure 2. Percent change in households by municipality for the “Trends of the 2000s”, “Smart Growth”, and “Sprawl” land use scenarios. (Click each image to expand.)
Transportation Pricing Scenarios
This analysis focused on three main transportation pricing variables: the level of the statewide tax on motor gasoline; a per-mile tax on vehicle miles traveled, and a regionwide congestion fee on freeways, with the congestion fee varying as a function of the level of traffic congestion. While many other pricing and policy variables are available in VisionEval, we chose these to begin evaluating the relative impact of various approaches. We selected different combinations of the value of these parameters to create five future scenarios for evaluation.
Our pricing scenarios tested three future levels of the statewide tax on motor gasoline: $0.24 per gallon (2020-level), $0.42 per gallon, and $0.75 per gallon, and two alternatives for a per-mile tax on vehicle miles travelled: $0.10 per mile and $0.25 per mile. We also tested the presence of a set of congestion fees on all freeways in the model region, with the level of the fee tied to the intensity of congestion occurring on the road, starting at $0.10 per mile for moderate congestion conditions, rising to $0.25, $0.50, and $1.00 per mile as congestion rose to heavy, severe, and then extreme levels. While we fully recognize the political and practical challenges with adopting many of the policies explored in this study, the three sets of pricing policies and their levels were chosen to explore the more ambitious limits of the price impacts on household VMT. None of these scenarios constitute a specific policy recommendation on MAPC’s behalf, instead providing waypoints on the path to large-scale reductions in regional VMT for the purpose of this analysis.
Results and Analysis
The scenarios produce a wide range of forecasted VMT levels in the year 2030, as shown in Table 1. The business-as-usual scenario, based on the Trends of the 2000s land use and no changes in transportation pricing, is estimated to result in an increase of 9.5 million household VMT per day, a growth of 21% over 2010 levels (estimated at 44.5 million VMT per day). With this level of VMT growth, technological improvements to vehicle fuel economy would have to yield an 18% reduction in per-mile carbon intensity just to break even on GHG emissions. Note that the VMT estimates produced in this study refer solely to household travel, and thus will differ from the total VMT numbers reported by MassDOT or the Boston MPO.
Since the average fuel efficiency of the Massachusetts passenger vehicle fleet was about 24.5 miles per gallon in 2010, it would need to rise to an effective fleet-wide average of 29 miles per gallon in 2030 in order to achieve a break-even point with carbon emissions. Whether that level of reduction is possible depends on a variety of factors: federal fuel efficiency standards for new cars, the rate of fleet turnover, the share of electric vehicles, and the carbon intensity of the electrical grid. Even if the Obama-era Corporate Average Fuel Efficiency (CAFE) standards were reinstated, fleet turnover would also have to increase substantially in order to achieve this target in the next 10 years. The scope of the technical improvements necessary to achieve GHG reductions (or merely restrain GHG growth) in the face of rising VMT underscores the importance of land-use policies that support VMT reductions. These reductions will be essential to fully reap the benefits of vehicle efficiency improvements in the timeframe needed to achieve the Commonwealth’s ambitious carbon mitigation targets.
The results of the other two land use scenarios also indicate the significant role land use patterns play in regionwide VMT growth. Relative to 2010, the base Smart Growth scenario (with no pricing changes) predicts an increase in VMT of 19.0%, the base Trends of the 2000s scenario predicts an increase in VMT of 21.4%, and the base Sprawl scenario predicts an increase in VMT of 24.2%. This 5.2% spread in the VMT growth rate between the Sprawl and Smart Growth scenarios is equivalent to nearly 2.3 million vehicle miles travelled per day. As noted previously, these forecasts do not account for any land use policies that would target growth to specific locations within a Community Type, so the differences among them are conservative, but the relative impact of the Smart Growth scenario on VMT is still quite substantial.
As expected, the model also forecasts that certain transportation pricing policies would have a substantial effect on future VMT, especially when applied in combination. We first explored the isolated impacts of increases in the gas tax of 18 and 51 cents above the current level of 24 cents per gallon. The resulting changes under the 18-cent increase were surprisingly small, with less than a 0.5% reduction in 2030 VMT compared with the scenario of no gas tax hike (Table 1). When the tax was raised 51 cents to 75 cents per gallon, VMT growth was reduced by 1.5% for the Trends of the 2000s and Smart Growth scenarios and by 1.2% for the Sprawl scenario. Even these reductions are smaller than the difference between the Smart Growth and Sprawl scenarios when no increase in the gas tax occurs. This means that the land use patterns can have an even larger effect than modest pricing policies: the Smart Growth scenario with the existing gas tax is better at curbing VMT growth than is a tripling of the gas tax under a Trends of the 2000s land use scenario. This result clearly demonstrates the substantial value of focused land use policies in restraining VMT growth, regardless of additional pricing policies.
In the next scenarios we modeled the combined effect of the 75 cent per gallon gas tax with either a $0.25 per mile VMT tax, a tiered freeway congestion pricing fee, or the combination of both. For all of the land use scenarios, the separate addition of either the freeway congestion pricing or the 25 cents per mile VMT tax produced similar reductions in VMT, with the VMT tax having a slightly stronger effect (Figure 3). While neither additional policy produced net reductions in 2030 VMT compared with 2010, VMT is projected to grow by only 2.8% in the case of the Smart Growth scenario with a 25 cent VMT fee and a 75 cent gas tax (Figure 3). For the Sprawl scenario with those same pricing policies, VMT could grow by 7.3%.
These results indicate that fees must be set at relatively high levels to reduce significantly the growth in VMT, let alone to achieve any net reductions in VMT. This is due to the low price-elasticity of vehicle travel demand – a known feature of travel behavior that can be attributed partially to the lack of competitive alternative modes of travel in much of the region. Our analysis indicates that even a tripling of the gas tax may have only a modest effect on VMT, whereas VMT fees are a much stronger price signal.
Why is this? Assuming pump prices (including tax) of $3.00 per gallon, fuel costs for a passenger vehicle with an average real-world fuel economy of 30 miles per gallon are roughly 10 cents per mile. A 51-cent increase in the gas tax would raise per-mile fuel costs to 11.7 cents per mile. For someone who drives 10,000 miles per year, this change would increase their fuel costs by only $170 annually, not enough to prompt significant changes in the behavior of most drivers. Furthermore, the price signal of a per-gallon gas tax will weaken as drivers acquire more fuel-efficient vehicles. In contrast, a VMT fee of 25 cents per mile, applied to 10,000 miles of driving, would add $2,500 to annual operating costs – an amount significant enough to induce changes in where people live, how much they travel, and what travel mode they choose.
Figure 3. Percent change in household daily vehicle miles traveled between 2010 and 2030 for different land use and transportation pricing policy combinations. Labels on X-axis describe scenario conditions in 2030.
The most ambitious scenario we modeled combines the $0.75 per gallon fuel tax, $0.25 per mile VMT tax, and freeway congestion pricing, which result in a substantial reduction in forecasted 2030 VMT below 2010 levels. The reductions are again differentiated by the three land use scenarios: Smart Growth VMT falls to 35.8 million per day (19.7% below 2010 levels), while Sprawl VMT falls to 37.0 million (16.9% below 2010). It is again worth noting that even with these ambitious pricing policies in place, the land use benefits of the Smart Growth scenario persist: 1.2 million fewer daily VMT compared to the Sprawl scenario.
|Description||DVMT 2030||% change from 2010|
|"Trends of the 2000s"|
|$0.24 gas tax||54,121,000||21.4|
|$0.42 gas tax||53,685,000||20.5|
|$0.75 gas tax||53,559,000||20.2|
|$0.75 gas tax + freeway congestion fees||47,719,000||7.1|
|$0.75 gas tax + $0.25 VMT fee||46,746,000||4.9|
|$0.75 gas tax + $0.10 VMT fee + freeway congestion fees||42,676,000||-4.2|
|$0.75 gas tax + $0.25 VMT fee + freeway congestion fees||36,340,000||-18.5|
|$0.24 gas tax||55,338,000||24.2|
|$0.42 gas tax||55,226,000||23.9|
|$0.75 gas tax||54,685,000||22.7|
|$0.75 gas tax + freeway congestion fees||47,855,000||7.4|
|$0.75 gas tax + $0.25 VMT fee||47,825,000||7.3|
|$0.75 gas tax + $0.10 VMT fee + freeway congestion fees||43,571,000||-2.2|
|$0.75 gas tax + $0.25 VMT fee + freeway congestion fees||37,057,000||-16.9|
|$0.24 gas tax||53,047,000||19.0|
|$0.42 gas tax||52,933,000||18.8|
|$0.75 gas tax||52,380,000||17.5|
|$0.75 gas tax + freeway congestion fees||46,864,000||5.2|
|$0.75 gas tax + $0.25 VMT fee||45,827,000||2.8|
|$0.75 gas tax + $0.10 VMT fee + freeway congestion fees||42,881,000||-3.8|
|$0.75 gas tax + $0.25 VMT fee + freeway congestion fees||35,789,000||-19.7|
Table 1. Summary of all scenario results. Daily vehicle miles traveled (DVMT) by households in the MAPC region. DVMT from commercial and other non-household vehicles is not included.
Conclusions and Next Steps
The combination of land use and transportation pricing scenarios described in this report allowed MAPC to examine the relative impact of different policies on changes in VMT over a relatively short time horizon. Additional scenario testing is ongoing to explore the trends and impacts on travel behavior of the COVID-19 pandemic, including targeted growth within community types and individual municipalities and a range of post-COVID recovery scenarios. As VMT continues to recover from the lockdown lows of early 2020, it is important to assess what policies might be effective in shifting the region away from a return to the congested, high-VMT era that preceded the pandemic. Over the medium-term time horizon we are also testing additional policies expected to influence VMT in a more targeted fashion, such as parking pricing, changes in transit service and accessibility, increased telecommuting or part-time remote work, and more targeted fees (e.g., for ride-sharing services). While many of these policies have the potential to encourage substantial changes in household travel behavior and VMT, the analysis in this report underscores the vital importance of trends in housing development and residential location choice in determining future levels of personal vehicle travel. Therefore, as we seek to reduce GHG emissions from the transportation sector and decrease VMT as one tool in this pursuit that also generates co-benefits from less congestion and better air quality, it is clear that a smart growth strategy for housing and mixed-use development is a powerful ally.
The fundamental allocation of people, households, and jobs across the region sets the conditions in which household travel decisions are made, and as seen in the Sprawl scenario, this can lead to a significant erosion of the impacts of other transportation or pricing policies. It is imperative that decisions on regional land use remain central to policy discussions on VMT and greenhouse gas emissions reductions. What our analysis also makes clear is that, in the absence of major interventions, growth in VMT will make it substantially harder for technological improvements in fuels and efficiency to achieve GHG reduction targets. Ambitious reductions in regional VMT will require ambitious policy interventions, some of which will have impacts well beyond the targeted activity of household travel behavior. These ambitious pricing policies risk exacerbating existing economic inequalities in mobility and accessibility, whereas the land use policies can be implemented in ways that benefit low-income and communities of color by expanding housing opportunity and job accessibility. In fact, more efficient land use can reduce transportation cost burden for low-income households, even in suburban locations. Explicit considerations should be made of the uneven socioeconomic effects of potential policies as they are being considered and designed with an eye toward advancing a suite of policies that achieve multiple wins in climate, public health, equity, and quality of life.
1 VisionEval is an open-source transportation scenario planning and modeling platform. Originally developed by a partnership between the Oregon Department of Transportation and the Federal Highway Administration, it is now supported and maintained by the Collaborative Development of New Strategic Planning Models Pooled Fund, hosted by FHWA. Model development is open-source and ongoing, with members of the Pooled Fund including several state DOTs and MPOs. MAPC is currently in the process of joining the Pooled Fund. The open-source nature of VisionEval has allowed MAPC to develop a customized implementation of the VisionEval Regional Scenario Planning Model (VERSPM), adapted to use the outputs of the UrbanSim land use, employment, and housing forecasts in place of the included VisionEval default land use data.