1 Analysis of Options for Reducing Transportation Greenhouse Gas Emissions in the Northeast and Mid-Atlantic Christopher D. Porter, Cambridge Systematics, Inc., 100 CambridgePark Drive, Suite 400, Cambridge, MA 02140, David Jackson, Cambridge Systematics, Inc., 4800 Hampden Lane, Suite 800, Bethesda, MD 20814, Gabriel Pacyniak, Georgetown Climate Center, 600 New Jersey Avenue, Washington DC 20001, Research Objectives Extended Abstract The objective of this research was to study options for reducing transportation greenhouse gas (GHG) emissions in the northeast and mid-atlantic regions of the United States. The research included development of a baseline transportation GHG inventory and forecast for the region, evaluation of the GHG reduction potential of a variety of existing and proposed measures, assessment of the feasibility of meeting various reduction targets in 2030, and modeling of the economic benefits to the region of a set of transportation carbon reduction policies. Background In the northeast and mid-atlantic, direct emissions from the transportation sector represent the largest source of GHG emissions approximately 35 percent of regional emissions in In 2010 the leaders of the transportation, energy, and environment agencies of 11 states in the northeast and mid-atlantic region and the District of Columbia joined to form the Transportation and Climate Initiative (TCI), with the Georgetown Climate Center as facilitator. The analysis described in this paper was commissioned to help state leaders better understand emissions trends and opportunities for reductions in the region. The analysis was designed by the Center and conducted by Cambridge Systematics, Inc. TCI member jurisdictions provided feedback on the analysis at various points throughout the process. In addition, the draft report was reviewed by an expert panel comprised of Dr. David Greene (University of Tennessee), Paula Hammond (WSP-Parsons Brinckerhoff), Roland Hwang (Natural Resources Defense Council), and Dr. Robert Noland (Rutgers University). Any errors in the analysis are the fault of the study authors alone and not of the reviewers or any staff from the participating TCI jurisdictions. The study is fully documented in a report (Georgetown Climate Center and Cambridge Systematics, 2014). Methodology Inventory and Forecast A region-wide GHG emissions inventory was developed for 2011 for the on-road, passenger rail and ferry sectors for the freight rail and intra-region marine sectors. A forecast was developed for the year Emissions between 2011 and 2030 were assumed to increase at a linear rate.
2 To develop the inventory and forecast for on-road sources, the U.S. Environmental Protection Agency s (EPA) MOVES model was used with inputs from representative counties. This approach ensured that unique features of emission profiles by different place type in the region could be captured in the analysis, while not requiring a distinct MOVES analysis for each individual county. Counties in the TCI region were classified by five place types based on population density --- core, high urban, medium urban, suburban, and rural. MOVES input files were obtained from selected states and GHG emission rates were developed by source type, road type, and place type. For 2011, these emission rates were then applied to VMT data obtained from the Highway Performance Monitoring System. Because the analysis was conducted before MOVES2014 was released, adjustments were applied to forecast emission rates to account for Federal light duty and heavy duty GHG/fuel economy standards that were not reflected in MOVES2010. To develop forecast emissions, VMT forecasts were obtained from each state. Because a significant range of VMT forecasting assumptions was observed, average growth rates across the region were applied to each place type. The 2011 passenger rail and ferry inventory is based on publically accessible data from the National Transit Database (NTD) and the Northeast Corridor Infrastructure Master Plan (NEC Master Plan Working Group, 2010). Passenger mile growth from 2011 to 2030 is based on a number of sources including historical growth from the NTD by operator and mode, and where available, ridership projections from operator specific long-range plans. The FHWA Freight Analysis Framework (FAF3) was used to estimate freight rail emissions. For trips with an external origin or destination, only 50 percent of tons and ton-miles (and associated emissions) were assigned to the TCI region. For intra-region marine, FAF data was supplemented with US Army Corps of Engineers Waterborne Commerce Statistics Center state-to-state flows by commodity and tonnage. Strategy Assessment The analysis modeled the GHG emissions reductions from a suite of transportation investments over the period 2015 to Three investment scenarios were modeled, ranging from $1.5 billion to $6.0 billion in average annual funding over the region. The clean transportation strategies included in each scenario and allocation of funding by strategy are shown in Table 1. Table 1 Investment Allocation by Strategy GHG Mitigation Strategy Allocation Financial incentives for purchase of electric vehicles and natural gas trucks 20.0% Investment in urban transit expansion 25.0% Promotion of urban infill and other compact land use 7.5% Investment in bicycle infrastructure in urban area 7.5% Additional support for travel demand management strategies 10.0% Additional investment in system operations efficiency technologies 15.0% Investment in infrastructure to support rail and intermodal connections 15.0% Total 100.0%
3 The impacts of a pricing mechanism (VMT fee, additional motor fuels tax, or carbon price) to raise the needed revenue for the above investment levels were also evaluated, along with the effect of a hypothetical regional clean fuels policy. A variety of methods were used to estimate the potential GHG benefits of each strategy at different levels of funding and reinvestment. Data from studies conducted within the TCI region were used to the extent possible to supplement national data on strategy impacts and effectiveness. A unique aspect of this study is that factors were developed to link GHG reductions to funding levels (e.g., tons of emissions reduced per dollar spent), so that different levels of pricing could be tested for their overall impact on emissions. A sampling of key assumptions follows: Electric and natural gas vehicles Subsidies were assumed to cover the forecast cost differential between electric and conventional vehicles (declining in the future) based on data from the California Air Resources Board (CARB) in support of the zero emission vehicle rulemaking (CARB 2011), and from literature on natural gas truck costs. The subsidy requirement considered 10 years of fuel savings as well as incremental vehicle and infrastructure costs. Life-cycle GHG emission rates were used from a study of low-carbon fuel options for the northeast states (NESCAUM 2011). Transit - The basic approach is to estimate the annual GHG benefit (in metric tons or tons) per dollar of capital investment. This was done by reviewing a sample of proposed transit projects in the Northeast and Mid-Atlantic regions for which data were available from project studies, and compared with other studies. A value of 25 tons per capital dollar was taken as the effectiveness estimate for year 2030 and adjusted in earlier years (up to 35 tons per dollar in 2015) to account for increasing automobile fuel efficiency. Transit benefits continue to occur in each year after the investment is made and therefore increase over time. Land use/smart growth To translate investment into impacts, the cost to shift one person or household from a dispersed land use type into a more compact land use type was used as a metric. A value of $1,200 per person ($3,000 per household) was used based on incentives provided by Massachusetts under its Chapter 40R smart growth incentive program (Commonwealth of Massachusetts 2013). Population was assigned to the five place types in the GHG inventory (based on census tract densities) and VMT per capita by place type was determined from the inventory. Bicycle investment A methodology similar to that used in Cambridge Systematics (2009) was applied. An increase in bicycle mode share (percent of trips) was assumed between current conditions and full build-out of a robust bike network. Different assumptions were applied to population by place type with the core place type increasing to 10 percent bicycle (trip) mode share at build-out. Unit cost and facility density estimates were applied to estimate the fraction of build-out achieved in each place type under each investment scenario. Travel demand management (TDM) -- A tons per dollar effectiveness was assumed based on evidence from a variety of national studies. A value of $100 per ton was used, which is equivalent to 10,000 tons reduced per million dollars spent annually. Unlike the capital investment strategies, TDM was assumed to have benefits only in the year the money is spent. System efficiency/operations GHG reductions per dollar of investment was also applied to this set of strategies. A value of $250 dollars per ton is used in this analysis, which equates to a value of about 300 tons per million dollars of capital investment. This value is used for 2015 and scaled down over time to account for increasing fuel efficiency. Freight/intermodal infrastructure and operations -- GHG effectiveness data on these strategies is limited to a handful of studies. For this analysis, a value of 140 tons per million capital dollars in 2015 was selected, which is based on the value from Cambridge Systematics (2009) for rail capacity improvements.
4 Pricing Price elasticities with respect to VMT and efficiency were derived from the 2014 Annual Energy Outlook Reference and High Oil Price scenarios to reflect the latest Federal standards. For many of the strategies, reported GHG cost-effectiveness can vary widely based on exactly how the money is spent and other key assumptions. For example, the literature on freight GHG costeffectiveness suggested a variation of two orders of magnitude. The cost-effectiveness numbers used in this study should therefore be taken as illustrative. The values selected are believed to be reasonably conservative and representative of likely impacts averaged across a wide range of projects. Economic Evaluation The Regional Economic Models, Inc. (REMI) Policy Insight model was used to evaluate the economic benefits. REMI is a dynamic model, measuring interactions among all sectors of the economy over time. Economic benefits are measured in terms of new jobs, additional gross regional product (GRP), and additional disposable income over the analysis period ( ). The economic analysis considered the net economic effects to the region from the following impacts: Travel time savings accruing to businesses, due to reductions in congestion and delay from traffic flow improvements and VMT reduction. These include time savings for truckers, other commercial vehicle operators, and other on-the-clock travel. Savings in fuel and vehicle maintenance (for businesses and consumers), as a result of strategies that allow travelers to reduce VMT. Shipping cost savings for businesses that can ship by rail rather than truck, as a result of improved freight rail infrastructure. Increased spending on vehicles (for electric vehicle and natural gas truck purchases) and electricity and natural gas to run these vehicles; offset by reduced petroleum fuel costs. New government investment in transportation infrastructure and services, made possible by the new funding mechanisms. Changes in consumer spending on non-transportation goods and services as a result of the net increase or decrease in other costs (vehicle purchase, fuel savings, taxes/fees, etc.) A variety of additional assumptions were needed to translate the basic assumptions for estimating GHG benefits into the above economic factors. For example, hours of delay reduced per VMT and per investment in system operations/efficiency measures were estimated based on TTI (2012). Values of travel time are from U.S. Department of Transportation guidance (USDOT 2011). A number of other costs were estimated and presented, but not included in the economic analysis because of difficulty in determining the appropriate method. These include air pollution costs, motor vehicle crash costs, pavement damage costs, and health cost savings due to increased physical activity. Reductions in petroleum use also were estimated. Findings and Conclusions Key findings from the analysis include the following: Existing federal and state policies are projected to achieve significant transportation emission reductions of about 29 percent by 2030 from 2011 levels, but additional policies would be needed to achieve greater cuts and to meet previous state commitments to reduce carbon pollution economywide in the northeast.
5 Additional clean transportation investments in clean vehicles, reduced traffic congestion, freight rail and shipping, transit, efficient land-use policies, and cycling and walking would help states in the region reduce carbon emissions from transportation by 31 to 39 percent by 2030 from 2011 levels. These investments would reduce oil consumption by 4 to 27 percent beyond what would be achieved by existing federal and state policies, and achieve public health benefits, such as reductions in premature deaths and asthma cases valued at $114 million to $463 million in 2030 in current dollars. A transportation pricing policy, such as a carbon fee, mileage-based user fee, or emissions budget program, would increase the range of emission reductions to 32 to 40 percent in 2030, and could generate proceeds to fund the transportation investments. A suite of clean transportation strategies funded by a transportation pricing policy would create net economic benefits for the region. Over 15 years, businesses would save $29 billion to $55 billion and consumers would save $4 billion to $18 billion. Cost savings from reduced fuel consumption, congestion, and consumer incentives would more than offset increased vehicle costs and fees. Such changes would increase the gross regional product by $12 billion to $18 billion, increase personal disposable income by $9 billion to $14 billion, and create 91,000 to 125,000 new jobs. Table 2 shows a summary of GHG reduction estimates for various scenarios. Table 2 Summary Results by Scenario (GHG Emissions in mmt) Scenario With Pricing Effects Without Pricing Effects Funding Clean Fuels % vs Baseline Baseline Pre-MY2025 Standards With MY2025 Standards 2030 % vs Baseline , % , % $1.5 billion None , % , % 10% , % , % $3 billion None , % , % 10% , % , % 15% , % , % $6 billion 15% , % , % The information produced by this research supported policy-making. On November 24 th, 2015, six northeast and mid-atlantic jurisdictions Connecticut, Delaware, the District of Columbia, New York, Rhode Island, and Vermont announced that they would work together to explore potential marketbased mechanisms to reduce emissions from the transportation sector.
6 References Cambridge Systematics, Inc. (2009). Moving Cooler: An Analysis of Transportation Strategies for Reducing Greenhouse Gas Emissions, Prepared for Urban Land Institute. CARB California Air Resources Board (2011). Spreadsheet entitled techpackage08nov2011.xls developed in support of ZEV requirement, accessed October Commonwealth of Massachusetts (2013). 760 CMR 59.00: Smart Growth Zoning Overlay District. accessed July Georgetown Climate Center and Cambridge Systematics (2014). Reducing Greenhouse Gas Emissions from Transportation: Opportunities in the Northeast and Mid-Atlantic. NEC Master Plan Working Group (2010). The Amtrak Northeast Corridor Infrastructure Master Plan. NESCAUM Northeast States for Coordinated Air Use Management (2011), Economic Analysis of a Program to Promote Clean Transportation Fuels in the Northeast/Mid-Atlantic Region. TTI (2012) - Texas A&M Transportation Institute (2012). Urban Mobility Report. USDOT (2011) United States Department of Transportation (2011). Revised Departmental Guidance on Valuation of Travel Time in Economic Analysis (Revision 2 corrected).