Office of Planning, Environment, & Realty (HEP)
Planning • Environment • Real Estate
Prepared for:
Federal Highway Administration
Prepared by:
ICF International
9300 Lee Highway
Fairfax, VA 22031
Contact: Jeffrey Ang-Olson
ICF reviewed progress on climate action plans in all 50 states. For this exercise, we define a climate action plan (CAP) as a statewide document that presents distinct strategies to reduce GHG emissions from multiple sectors. ICF extracted from these plans:
All of the information gathered is provided in a separate Excel workbook.
This report provides a basic summary and analysis of the data gathered on transportation emission reduction strategies. It also assesses the level of certainty in estimates of strategies' impacts on GHG emissions.
Figure 1 and Table 1 summarize the status of climate plans by state. Twenty states have no climate action planning process or have not completed a CAP. Of these 20 states, three are in progress. Five states have U.S. EPA-funded research papers to propose state climate strategies, but they are older (circa 2000) and were not state-sponsored nor state adopted. The remaining 12 states have no climate plan.
Thirty states have a completed climate change plan. Of these 30, six state plans do not include any estimate of the impact of strategies on GHG emissions. Twenty-four states have a completed a plan that estimates the impact of strategies on GHG emissions.
Figure 1: Status of State Climate Action Plans, November 2009
Quantified impacts of strategies | Did not quantify impacts of strategies | No climate action plan, or action plan in progress (IP) | ||
|---|---|---|---|---|
Alaska Arkansas Arizona California Colorado Connecticut Florida Iowa Maine Maryland Michigan Minnesota Montana | New Hampshire New Mexico New York North Carolina Oregon Pennsylvania Rhode Island South Carolina Vermont Washington Wisconsin | Illinois Massachusetts Nevada New Jersey Utah Virginia | Alabama* Delaware* Georgia Hawaii (IP) Idaho (IP) Indiana Kansas (IP) Kentucky* Louisiana Mississippi Missouri* | North Dakota Nebraska Ohio Oklahoma South Dakota Tennessee* Texas West Virginia Wyoming |
* Aforementioned U.S. EPA-funded research papers.
To compare the relative impact of strategies in each state, ICF collected forecasts of GHG emissions for those states with a CAP. We generally collected both estimates of strategies' impacts and GHG forecasts for the year 2020. Several states did not provide impact estimates for 2020. For these states we collected both impact estimates and forecasts for the available year closest to 2020. A few states did not provide separate forecasts for on-road transportation GHG emissions. In these states we have used emissions from motor gasoline and diesel as a proxy for on-road emissions. Several states did not have any GHG forecasts readily available.
Table 2 below provides the forecast data collected for each state, with emissions in million metric tons of CO2-equivalent (MMtCO2e) emissions. Total GHG emissions vary widely by state, depending on the size of the population and the amount and type of industry in the state. Not surprisingly, smaller states and less-populated states tend to emit less GHGs.
| State | Data Year | On-road Emissions | Transportation Sector Emissions | Total GHG Emissions Forecast, All Sectors (MMtCO2e) Business as Usual | Total GHG Emissions Forecast, All Sectors (MMtCO2e) with Reductions | ||
|---|---|---|---|---|---|---|---|
| MMtCO2e | % of Transportation Sector | MMtCO2e | % of Total | ||||
| AK | 2020 | 4.4 | 22% | 20.4 | 34% | 60 | 49 |
| AR | 2025 | 27.3 | 88% | 31.1 | 27% | 114 | 56 |
| AZ | 2020 | 49.9 | 85% | 58.6 | 37% | 160 | 91 |
| CA | 2020 | 209 | 93% | 225 | 38% | 596 | 442 |
| CO | 2020 | 31.9* | 88% | 36.2 | 25% | 148 | 106 |
| CT | 2020 | 37 | |||||
| FL | 2025 | 167 | 84% | 200 | 52% | 386 | 165 |
| IA | 2020 | 25.0 | 92% | 27.2 | 20% | 139 | 31 |
| IL | 2020 | 312 | 276 | ||||
| MD | 2020 | 38.0 | 93% | 40.9 | 31% | 132 | |
| ME | 2020 | 11.1 | 88% | 12.6 | 44% | 29 | 26 |
| MI | 2025 | 60.1 | 92% | 65.3 | 22% | 292 | 166 |
| MN | 2025 | 31.9 | 82% | 38.8 | 19% | 201 | 112 |
| MT | 2020 | 9.3* | 89% | 10.4 | 25% | 42 | 30 |
| NC | 2020 | 74.4* | 91% | 81.5 | 32% | 256 | 137 |
| NJ | 2020 | 54 | 93% | 589 | 38% | 154 | 116 |
| NM | 2020 | 20.1 | 90% | 22.3 | 22% | 102 | 34 |
| NY | 2020 | 29.6 | 39% | 76 | 65 | ||
| OR | 2025 | 32.0 | 40% | 80 | |||
| PA | 2025 | 103 | 27% | 383 | 227 | ||
| SC | 2020 | 39.8 | 91% | 43.6 | 35% | 125 | 64 |
| UT | 2020 | 19.1* | 22.4 | 23% | 96 | ||
| VT** | 2030 | 4.6* | 94% | 4.9 | 42% | 12 | 1.4 |
| WA | 2020 | 41.1 | 72% | 56.9 | 47% | 122 | |
| WI | 2020 | 149 | |||||
Blank cells indicate information not available. Some states' forecasts do not incorporate the latest projections for fuel efficiency of light-duty vehicles. Expectations for fuel efficiency were revised substantially with the passage of the Energy Independence and Security Act of 2007.
*On-road emissions not available. Motor gasoline and diesel emissions used as a proxy.
**In Vermont only, the year of emission reduction estimates did not match the year of forecast. Reductions were estimated for 2028.
States have typically used similar, but not always identical, techniques to forecast their transportation GHG emissions. Key assumptions may vary from state to state. Forecasts of vehicle fuel efficiency are a primary factor in predicting on-road GHG emissions. Many states that produced CAPs after the passage of the 2007 Energy Independence and Security Act, which raised Corporate Average Fuel Economy Standards, have included revised fuel efficiency assumptions in their GHG forecasts. Inventories that do not incorporate the new CAFE standards likely overestimate emissions from light-duty vehicles.
The 30 state climate action plans examined include a total of 358 strategy recommendations to reduce GHG emissions from transportation. In many cases, strategies are very similar from state to state. To conduct an analysis across states and strategy types, ICF classified each strategy into one of 21 categories. Categories were defined in order to maximize common characteristics among strategies that are grouped together and minimize overlap between categories. Some overlap between categories is unavoidable. Some states have crafted strategies more broadly than others. Some strategies are focused on achieving GHG reductions from particular vehicle types, while others are focused on achieving reductions through particular incentives or outreach programs. Some strategies propose a suite of technology options, while other strategies propose more specific technology options.
In cases where a single strategy could fall into more than one category, we classified measures according to their primary focus. Some individual strategies are broadly defined, but only one part of the overall strategy design is quantified. In these cases we classified strategies according to the element quantified. While most strategies related to vehicle technologies focus on either light-duty or heavy-duty vehicles, a few address both. In these cases we classified the strategy according to the vehicle type of greater focus in the strategy language.
We defined the strategy categories as follows:
Tables 3, 4, and 5 show strategies by category and by state. Table 3 shows the number of strategies appearing in each plan. Table 4 shows emission reductions estimated for strategies in each plan. Table 5 shows emission reductions in each plan as a percentage of total transportation GHG emissions. Blank cells indicate that the state's plan did not include or did not quantify that strategy and/or no inventory figures were available. Rounding occurred as part of the presentation, so totals may not always add up.
Among the 30 state climate action plans we analyzed, the five most common strategies are:
Table 3 shows the frequency of each strategy in each state.
Alternative Fuels/Low Carbon Fuel Standard includes 41 individual strategies in 27 climate plans. A great deal of diversity spans this group of strategies. The plans for Colorado, Minnesota, South Carolina, and Washington include a Low Carbon Fuel Standard, modeled after a similar standard in California, which would mandate a 10% reduction in vehicle fuel carbon intensity without specifying a particular mix of fuels. Because the carbon reduction goal is written directly into the strategy, the GHG benefits are straightforward to calculate and relatively large. Other states (such as New Mexico) have adopted broad alternative fuels strategies that promote biofuels (ethanol and biodiesel), electric, and hybrid-electric vehicles. Estimated emissions reductions tend to be smaller for strategies that focus only on promoting ethanol (which, if produced from corn feedstocks, has a minimal lifecycle GHG benefit) and biodiesel blends.
Transit and Alternative Modes includes 34 strategies in 21 of the state plans. The approach is usually multi-pronged. It can include enhanced provision of infrastructure for multiple modes, such as new or increased transit service and stations, walking and biking paths, bike racks, and other types of facilities. In addition, the strategies often include packages for promotion of these modes such as public education, advertising and incentives.
Light-duty Vehicle New Vehicle Emissions Standards includes 28 strategies in 21 climate plans. Almost all states modeled strategies after the California Clean Car Program, which set tailpipe GHG emissions standards for new vehicles on a grams per mile basis. The Clean Air Act allows states to adopt California's emissions standards instead of federal emissions standards. After some substantial controversy about the right of states to regulate vehicles' GHG emissions, the Obama administration is now finalizing a new federal Corporate Average Fuel Economy (CAFE) standard that would achieve the same reduction in GHG emissions as California's near-term standard. While states have also won the right to enforce California's standards, the federal government has established leadership on GHG emissions standards, at least in the near-term. Any state standards are likely to produce only incremental reductions in GHG emissions, beyond the new CAFE standards.
Smart Growth Measures include 27 strategies in 21 plans. In some states (such as VT and NC), the strategies have set ambitious goals for reducing VMT by promoting more compact development patterns and by coordinating the provision of transportation infrastructure with development areas. In other states, the plans have included more modest proposals to promote efficient land use patterns largely within the confines of existing state and local policies. The reductions estimated for Colorado, for example, reflect modeling by the Denver MPO of a compact future land use scenario considered feasible given the current policies and political environment.
Light-duty Vehicle Clean Vehicle Purchase Incentives includes 25 strategies in 20 plans. The most ambitious of these strategies propose "feebates," through which states would charge consumers that purchase vehicles with higher emissions a surcharge and offer a rebate to consumers who purchase vehicles with lower emissions. Feebates would affect GHG emission through two mechanisms:
The shift of fleet mix is thought to account for around 90% of the benefit of the strategy. This is also the mechanism that will take longer to produce results. Many states have concluded that a feebate program would need to be implemented in cooperation across a number of states in order to have an appreciable affect on vehicle purchases. Some plans have proposed multi-state studies on the potential of feebates.
Other types of incentives target purchasers of hybrid and alternative fuel vehicles specifically. They include tax credits as well as preferential access to lanes and parking spaces. A few states have proposed charging additional registration fees for vehicles with high GHG emissions.
| Number of Individual Strategies | |||||||||||||||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Strategy Category | AK | AR | AZ | CA | CO | CT | FL | IA | IL | MA | MD | ME | MI | MN | MT | NC | NH | NJ | NM | NV | NY | OR | PA | RI | SC | UT | VA | VT | WA | WI | Total |
| Alt. Fuels/Low Carbon Fuel Std. | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 3 | 1 | 2 | 2 | 3 | 2 | 41 | |||
| Combined Smart Growth/Transit Measures | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 8 | ||||||||||||||||||||||
| Commuter Benefits/Trip Reduction Programs | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 17 | ||||||||||||||||
| Freight systems strategies | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 14 | |||||||||||||||||
| HDV anti-idling measures | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 18 | |||||||||||||
| HDV Retrofit or Replacement | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 13 | ||||||||||||||||||
| Integrate GHGs in Decision Making | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 10 | |||||||||||||||||||||
| LDV and HDV Fleet-based Measures | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 1 | 21 | |||||||||||||
| LDV Clean Vehicle Purchase Incentives | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 1 | 2 | 2 | 2 | 1 | 2 | 1 | 1 | 1 | 1 | 1 | 25 | ||||||||||
| LDV Efficiency Improvements | 1 | 1 | 2 | ||||||||||||||||||||||||||||
| LDV New Vehicle Emissions Stds. | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 2 | 1 | 2 | 1 | 3 | 1 | 1 | 1 | 2 | 1 | 28 | |||||||||
| LDV Tires | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 7 | |||||||||||||||||||||||
| Non-road Measures | 2 | 1 | 1 | 5 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 19 | |||||||||||||||||||
| Other | 2 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 2 | 1 | 15 | ||||||||||||||||||||
| Parking, Road, and Fuel Pricing | 1 | 1 | 4 | 2 | 1 | 1 | 1 | 1 | 12 | ||||||||||||||||||||||
| Pay as You Drive Insurance | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 12 | ||||||||||||||||||
| Public Education | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 9 | |||||||||||||||||||||
| Smart Growth | 1 | 1 | 1 | 1 | 2 | 2 | 1 | 1 | 1 | 2 | 1 | 1 | 1 | 3 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 27 | |||||||||
| Traffic Speed/Flow Measures | 1 | 1 | 1 | 1 | 2 | 1 | 2 | 2 | 1 | 1 | 2 | 1 | 1 | 1 | 1 | 19 | |||||||||||||||
| Transit and Alt. Modes | 1 | 1 | 1 | 1 | 2 | 2 | 3 | 3 | 1 | 1 | 1 | 6 | 2 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 1 | 34 | |||||||||
| Vehicle and Fuels R&D | 1 | 3 | 1 | 1 | 1 | 7 | |||||||||||||||||||||||||
| Total | 12 | 10 | 13 | 9 | 11 | 9 | 7 | 11 | 6 | 21 | 9 | 10 | 9 | 12 | 13 | 13 | 26 | 22 | 12 | 9 | 8 | 17 | 5 | 9 | 13 | 12 | 14 | 9 | 16 | 11 | 358 |
Twenty-four plans include estimated GHG reductions by strategy. Table 4 shows the effectiveness of strategies in each state plan, in million metric tons of CO2-equivalent emissions (MMtCO2e) avoided. The gross total GHG reduction from these strategies is 332 MMtCO2e nationwide by 2020.
In nearly every state, there is overlap among strategies, so the GHG impacts are not purely additive. In some states, the total net reduction (accounting for overlap) has been estimated, as shown in the last row in Table 4. Comparing the gross total and the net total in these states, we estimate that the overlap reduces the total GHG reduction by an average of 15%. So the net reduction from the transportation strategies in the 24 states in Table 4 would be approximately 281 MMtCO2e in 2020.
If we assume that the remaining 26 states are capable of achieving GHG reductions similar to those of the 24 states in Table 4, then the total potential U.S. transportation reductions from state climate plans would be on the order of 560 MMtCO2e in 2020. This reduction represents 29% of the projected baseline (business as usual) GHG transportation sector emissions for 2020, according to DOE's Annual Energy Outlook. [1]
We compared the estimated emission reductions for different strategy types. For 19 of the 24 plans with quantified strategies, we obtained transportation GHG forecasts, which allow us to calculate the impact of strategies normalized by total transportation emissions in the state. Table 5 shows the effectiveness of strategies in each plan, relative to the state's total forecast emissions. Where a state plan includes more than one strategy in a single category, figures include the impacts of multiple strategies. For example, Arizona's three Alternative Fuels/Low Carbon Fuel Standard strategies would reduce emissions by 1.1 MMtCO2e, or 2% of total transportation GHG emissions in Arizona.
The effectiveness of strategies varies widely even within categories. For example, Arkansas and Iowa estimated that Combined Smart Growth/Transit Measures could reduce transportation GHG emissions by 1%. Vermont estimated that Combined Smart Growth/Transit Measures could reduce transportation GHG emissions by 20%. This range of variation reflects the diversity of strategies between states, as well as the diversity of assumptions used to estimate reductions (see Section 4 for more information on these assumptions).
The five strategy types with the highest average estimated impact on transportation GHG emissions, normalized by the transportation sector emissions forecast, are:
State plans estimated that LDV New Vehicle Emissions Standards would reduce between 0.2% and 14% of state transportation GHG emissions. The total reductions from such strategies across all states are estimated to be 87 MMtCO2e. The largest reductions were in California (32 MMtCO2e) and Pennsylvania (14 MMtCO2e). The smallest reductions were in Washington (0.4 MMtCO2e) and Iowa (0.8 MMtCO2e). Results vary based on the type of standard, assumptions about market penetration of new vehicles, and assumptions about baseline fuel efficiency improvements.
State plans estimated that Alternative Fuels/Low Carbon Fuel Standards would reduce between 0.02% and 19% of state transportation GHG emissions. The total reductions from such strategies across all states are estimated to be 85 MMtCO2e in reductions. Estimated reductions range from 16 MMtCO2e in California to 0.04 MMtCO2e in Montana. Results vary based on the design of strategies, market penetration of alternative fuels and vehicles, and the focus of strategies on particular fuel and vehicle types.
State plans estimated that Combined Smart Growth/Transit Measures would reduce between 1% and 20% of state transportation GHG emissions. The total reductions from such strategies across all states are estimated to be 9 MMtCO2e. Estimated reductions range from 6 MMtCO2e in Pennsylvania to 0.1 MMtCO2e in Rhode Island. Results vary based on the design of strategies and assumptions about the scale of change in transportation and land use patterns.
State plans estimated that Smart Growth Measures would reduce between 0.2% and 11% of state transportation GHG emissions. The total reductions from such strategies across all states are estimated to be 40 MMtCO2e. North Carolina has the highest estimated reduction at 8 MMtCO2e whereas Alaska is the lowest with 0.04 MMtCO2e. One factor contributing to the variation in results are the varying proportions of urban travel form state to state. For example, only 23% of Montana's VMT occurs in urban areas whereas 62% of North Carolina's VMT occurs in urban areas. Smart growth strategies typically target urban VMT.
State plans estimated that Pay as You Drive Insurance (PAYD) would reduce between 3% and 11% of transportation GHG emissions. The total reductions from such strategies across all states are estimated to be 18 MMtCO2e. North Carolina has the highest estimated reduction at 5 MMtCO2e; New Hampshire and Vermont have the lowest estimated reductions at 0.3 MMtCO2e.
In PAYD, drivers pay for insurance based on the amount of miles that they drive. Thus drivers are financially rewarded for driving less. Variation in effectiveness across states is generally due to assumptions about market penetration. Some plans propose making PAYD mandatory, in which cases penetration would reach 100%. The Colorado plan would not make PAYD mandatory. Both ultimate penetration and effectiveness are lowest in that state.
| Strategy Category | AK | AR | AZ | CA | CO | CT | FL | IA | MD | ME | MI | MN | MT | NC | NH | NM | NY | OR | PA | RI | SC | VT | WA | WI | Total |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alt. Fuels/Low Carbon Fuel Std. | 0.11 | 1.1 | 15.5 | 2.2 | 12.6 | 5.1 | 1.9 | 0.8 | 5.9 | 3.6 | 0.0 | 4.5 | 0.9 | 1.7 | 0.6 | 0.8 | 12.0 | 0.1 | 4.3 | 0.4 | 5.1 | 5.8 | 85 | ||
| Combined Smart Growth/Transit Measures | 0.2 | 0.5 | 0.2 | 1.1 | 6.0 | 0.1 | 1.0 | 9 | |||||||||||||||||
| Commuter Benefits/Trip Reduction Programs | 0.0 | 0.5 | 0.0 | 0.4 | 0.1 | 0.3 | 0.1 | 0.4 | 0.2 | 2 | |||||||||||||||
| Freight systems strategies | 0.5 | 3.5 | 0.1 | 1.1 | 0.6 | 0.5 | 0.1 | 0.5 | 0.2 | 7 | |||||||||||||||
| HDV anti-idling measures | 0.01 | 1.3 | 0.1 | 0.0 | 0.8 | 0.0 | 0.2 | 0.0 | 0.7 | 0.1 | 0.2 | 3 | |||||||||||||
| HDV Retrofit or Replacement | 0.09 | 0.0 | 0.9 | 2.4 | 0.7 | 0.0 | 2.2 | 0.8 | 0.2 | 0.4 | 8 | ||||||||||||||
| Integrate GHGs in Decision Making | 0.04 | 6.8 | 1.7 | 9 | |||||||||||||||||||||
| LDV and HDV Fleet-based Measures | 0.03 | 0.0 | 0.4 | 0.1 | 0.0 | 0.9 | 2 | ||||||||||||||||||
| LDV Clean Vehicle Purchase Incentives | 0.1 | 1.6 | 3.7 | 0.0 | 2.3 | 1.6 | 0.5 | 0.1 | 0.6 | 0.3 | 11 | ||||||||||||||
| LDV General Efficiency Improvements | 4.5 | 0.2 | 5 | ||||||||||||||||||||||
| LDV New Vehicle Emissions Stds. | 5.6 | 31.7 | 3.4 | 3.1 | 0.8 | 1.0 | 1.2 | 0.9 | 8.1 | 3.6 | 1.9 | 2.6 | 4.7 | 14.0 | 1.1 | 0.4 | 2.6 | 87 | |||||||
| LDV Tires | 0.8 | 1.8 | 0.7 | 0.0 | 0.6 | 0.1 | 1.0 | 5 | |||||||||||||||||
| Non-road Measures | 0.02 | 0.2 | 0.2 | 0.1 | 1.6 | 2 | |||||||||||||||||||
| Other | 0.4 | 1.0 | 0.1 | 2 | |||||||||||||||||||||
| Parking, Road, and Fuel Pricing | 0.0 | 4.7 | 1.0 | 1.0 | 7 | ||||||||||||||||||||
| Pay as You Drive Insurance | 2.8 | 0.9 | 4.3 | 0.4 | 2.1 | 5.3 | 0.3 | 1.0 | 0.4 | 0.3 | 18 | ||||||||||||||
| Public Education | 2.2 | 2 | |||||||||||||||||||||||
| Smart Growth | 0.04 | 4.0 | 5.0 | 0.5 | 3.5 | 4.6 | 0.3 | 0.4 | 1.9 | 0.1 | 8.0 | 0.2 | 1.3 | 0.3 | 2.3 | 1.6 | 5.8 | 40 | |||||||
| Traffic Speed/Flow Measures | 0.01 | 0.5 | 7.0 | 0.2 | 0.5 | 0.3 | 0.3 | 0.1 | 0.2 | 0.5 | 9 | ||||||||||||||
| Transit and Alt. Modes | 0.00 | 0.0 | 1.0 | 1.0 | 0.0 | 3.1 | 0.5 | 0.3 | 5.8 | 0.4 | 0.1 | 0.0 | 0.3 | 3.6 | 1.3 | 18 | |||||||||
| Vehicle and Fuels R&D | 2.5 | 0.3 | 3 | ||||||||||||||||||||||
| Total Without Overlaps | 0.4 | 3 | 16 | 62 | 9 | 6 | 28 | 11 | 19 | 3 | 11 | 10 | 1 | 36 | 10 | 8 | 5 | 6 | 33 | 1 | 9 | 3 | 19 | 21 | 332 |
| Total Accounting for Overlaps | 0.3 | 3 | 15 | N/A | 8 | N/A | 25 | 11 | 19 | N/A | 11 | 9 | 1 | 26 | N/A | 7 | N/A | N/A | N/A | N/A | 6 | 3 | 11 | N/A |
| Strategy Category | AK | AR | AZ | CA | CO | FL | IA | MD | ME | MI | MN | MT | NC | NM | NY | OR | PA | SC | VT | WA | Min | Avg | Max |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Alt. Fuels/Low Carbon Fuel Std. | 1% | 2% | 7% | 6% | 6% | 19% | 5% | 6% | 9% | 9% | 0% | 6% | 8% | 2% | 2% | 12% | 10% | 9% | 9% | 0% | 7% | 19% | |
| Combined Smart Growth/Transit Measures | 1% | 1% | 4% | 6% | 20% | 1% | 6% | 20% | |||||||||||||||
| Commuter Benefits/Trip Reduction Programs | 0% | 1% | 0% | 1% | 1% | 0% | 1% | 4% | 0% | 1% | 4% | ||||||||||||
| Freight systems strategies | 2% | 2% | 1% | 2% | 1% | 0% | 1% | 2% | 1% | 0% | 0% | 1% | 2% | ||||||||||
| HDV anti-idling measures | 0% | 2% | 0% | 0% | 1% | 0% | 0% | 0% | 3% | 0% | 0% | 0% | 1% | 3% | |||||||||
| HDV Retrofit or Replacement | 0% | 0% | 0% | 6% | 0% | 3% | 0% | 0% | 1% | 0% | 1% | 6% | |||||||||||
| Integrate GHGs in Decision Making | 0% | 0% | 0% | 0% | 12% | 0% | 2% | 12% | |||||||||||||||
| LDV and HDV Fleet-based Measures | 0% | 0% | 0% | 1% | 0% | 0% | 0% | 0% | 0% | 0% | 1% | ||||||||||||
| LDV Clean Vehicle Purchase Incentives | 0% | 0% | 0% | 1% | 13% | 0% | 0% | 3% | 0% | 0% | 0% | 13% | 0% | 3% | 13% | ||||||||
| LDV General Efficiency Improvements | 2% | 1% | 1% | 1% | 2% | ||||||||||||||||||
| LDV New Vehicle Emissions Stds. | 10% | 14% | 9% | 3% | 8% | 3% | 9% | 10% | 9% | 9% | 15% | 14% | 3% | 1% | 0% | 8% | 14% | ||||||
| LDV Tires | 1% | 1% | 2% | 0% | 3% | 0% | 1% | 0% | 1% | 3% | |||||||||||||
| Non-road Measures | 0% | 0% | 0% | 0% | 4% | 0% | 0% | 1% | 4% | ||||||||||||||
| Other | 1% | 0% | 0% | 0% | 0% | 0% | 1% | ||||||||||||||||
| Parking, Road, and Fuel Pricing | 0% | 11% | 0% | 2% | 0% | 3% | 11% | ||||||||||||||||
| Pay as You Drive Insurance | 5% | 3% | 11% | 3% | 5% | 7% | 4% | 7% | 3% | 5% | 11% | ||||||||||||
| Public Education | 0% | 0% | 3% | 0% | 0% | 3% | 1% | 3% | |||||||||||||||
| Smart Growth | 0% | 7% | 2% | 1% | 2% | 11% | 2% | 1% | 5% | 1% | 10% | 6% | 1% | 5% | 3% | 0% | 4% | 11% | |||||
| Traffic Speed/Flow Measures | 0% | 1% | 3% | 0% | 1% | 0% | 1% | 0% | 0% | 0% | 0% | 1% | 3% | ||||||||||
| Transit and Alt. Modes | 0% | 0% | 0% | 0% | 3% | 0% | 8% | 1% | 1% | 7% | 0% | 0% | 7% | 6% | 0% | 2% | 7% | ||||||
| Vehicle and Fuels R&D | 0% | 8% | 0% | 1% | 1% | 2% | 8% | ||||||||||||||||
| Total without Overlaps | 2% | 10% | 28% | 28% | 24% | 14% | 41% | 47% | 26% | 16% | 27% | 12% | 45% | 36% | 18% | 19% | 32% | 20% | 63% | 34% | |||
| Total Accounting for Overlaps | 1% | 10% | 26% | N/A | 22% | 12% | 40% | 46% | N/A | 17% | 23% | 10% | 32% | 31% | N/A | N/A | N/A | 14% | 61% | 19% |
The estimates of emission reductions in state CAPs are subject to uncertainty. The actual reductions from the strategies proposed in state CAPs will depend upon:
This section analyzes the strategy design and quantitative estimates of strategies in order to assess the likelihood of states achieving the reductions they have estimated. While the certainty of achieving estimated reductions cannot be definitively quantified, we use several different rating criteria in order to call out those strategy types that are most likely to achieve estimated reductions and, conversely, those strategies subject to the most uncertainty.
When considering the uncertainties in state CAP GHG estimates, it is important to keep in mind the purpose of the plans. Most state CAPs are the product of a collaborative process through which stakeholders make recommendations for state-led GHG mitigation actions. In general, the stakeholders involved share a common goal: to reduce GHG emissions in their state as much as possible. State CAPs are not fiscally constrained, nor are they necessarily constrained by existing limits on implementing authority. Stakeholders leading many state CAPs seek to "push the envelope" by encouraging their state to dedicate more funding or establish greater powers for GHG reduction strategies than currently available. Thus, CAPs are fundamentally different from long-range transportation plans, which are fiscally constrained and developed largely by the entities (MPOs, DOTs) that would implement the plans.
We evaluated a subset of measures from CAPs according to the following characteristics:
We evaluated quantified strategies from a sample of nine states according to these five characteristics. The Center for Climate Strategies (CCS), a non-profit organization, has produced the majority of completed U.S. state climate plans. These plans use consistent methods to develop and estimate the impacts of GHG reduction measures, and many of the measures included in the plans are similar. Five states in our sample have plans led by CCS; the remaining four state plans were not led by CCS. The full sample includes both populous and less populous states, states with and without major metropolitan areas, and states in all geographic regions of the country. The evaluated sample of states is provided below:
A total of 84 quantified strategies from these state CAPs were evaluated based on written information provided in the text of CAPs, supporting documents (available online), and the knowledge of our analysts. CCS plans tend to provide the most thorough description of strategies and quantification methods. In addition, because ICF evaluated strategies for many of the CCS plans, we are familiar with the specific data sources and analytical methods used. For the other plans, there was much less information available to inform our evaluations. In some cases, we have filled information gaps by assuming that strategies are structured, implemented, and evaluated in the same way as similar strategies in other state plans. Specific assumptions made and rating categories used are described in more detail in the following subsections.
Each strategy can be classified by the mechanism through which it reduces GHG emissions - its policy lever. We assigned strategies to one or more of the following categories of policy lever:
Policy levers are readily apparent from the basic design of each strategy. More than one policy lever applies to some individual strategies, so the sum of the rows is greater than the total. Table 6 shows the distribution of the 84 evaluated strategies among the policy levers.
| No. of Strategies (% of total) | MMtCO2e Reduced (% of total) | |
|---|---|---|
| Reduce VMT | 35 (42%) | 37.1 (28%) |
| Improve System Operation | 16 (19%) | 10.8 (8%) |
| Clean Fuels and Technologies | 52 (62%) | 101.7 (76%) |
| All Strategies in Sample | 84 (100%) | 134.6 (100%) |
About three quarters (76%) of the tons reduced are associated with strategies intended to promote clean fuels and technologies in cars and trucks, non-road vehicles, or supporting infrastructure. About one quarter (28%) of the tons reduced is associated with strategies intended to reduce VMT. These include those strategies that shift modal or physical travel patterns, including smart growth strategies, transit, bike & pedestrian strategies, strategies that change the price of driving, and other strategies that encourage the use of alternative modes. A small share of tons reduced (8%) is associated with strategies that affect system operation.
A few types of strategies in state CAPs would reduce GHG emissions through multiple policy levers. For example, measures that reduce vehicle idling and improve traffic speed and flow typically involve changes to both technologies and vehicle operations. Several broad strategies that target entire fleets of vehicles and even entire goods movement systems would involve all three policy levers.
Table 7 shows the distribution of CAP GHG reductions in the nine sample states across the three policy levers. (The column sum is greater than the total because some strategies involve more than one lever.) Washington and Minnesota stand out as relying on VMT reduction for at least half of their plan benefits. In contrast, Iowa, Oregon, and California rely heavily on clean fuels and technologies strategies.
|
| Reduce VMT | Improve System Operations | Clean Fuels and Technologies | Total |
|---|---|---|---|---|
| California | 9.5 (15%) | 8 (13%) | 56.3 (90%) | 62.3 (100%) |
| Colorado | 2.9 (33%) | 0.1 (1%) | 5.7 (67%) | 8.6 (100%) |
| Iowa | 0.9 (8%) | 0.6 (6%) | 10.8 (97%) | 11.1 (100%) |
| Maine | 0.7 (21%) | 0 (1%) | 2.6 (79%) | 3.2 (100%) |
| Minnesota | 5.2 (50%) | 0.9 (8%) | 5.2 (51%) | 10.3 (100%) |
| New York | 1.6 (30%) | 0.3 (7%) | 3.7 (70%) | 5.2 (100%) |
| Oregon | 0.3 (5%) | 0.1 (1%) | 5.7 (95%) | 6 (100%) |
| South Carolina | 2.8 (32%) | 0.3 (4%) | 5.8 (67%) | 8.7 (100%) |
| Washington | 13.3 (69%) | 0.4 (2%) | 5.9 (31%) | 19.2 (100%) |
Each type of policy lever is associated with different challenges and uncertainties in enacting strategies and achieving forecast reductions. Strategies that seek to reduce VMT generally require a behavioral change, and they therefore may encounter political resistance or have difficulty achieving the necessary market penetration. Strategies that seek to change technologies or fuels can be appealing because they typically do not require behavioral changes. But these measures may encounter opposition from producers of vehicles and fuels, and may rely on speculative technological breakthroughs. Strategies that change system operations typically encounter little political resistance. However, the effect of these strategies on GHG emissions is often highly uncertain.
Requirements for enactment describe what the state needs in order to enact strategies as written. Possible requirements for enactment include:
All the strategies we evaluated in our sample have at least one requirement for enactment, and some have up to four. Table 8 below shows the distribution of the 84 evaluated strategies by these four types of requirements.
| No. of Strategies (% of total) | MMtCO2e Reduced (% of total) | |
|---|---|---|
| Public Funding | 50 (60%) | 38.9 (29%) |
| Legislation or Rulemaking | 65 (77%) | 126.7 (94%) |
| Major Public Agency Initiative | 29 (35%) | 33.6 (25%) |
| Private Industry Collaboration | 33 (39%) | 26.4 (20%) |
| All Strategies in Sample | 84 (100%) | 134.6 (100%) |
More than three quarters of strategies and nearly all (94%) of the tons of reduction estimated depend upon some new legislation or rulemaking. Within almost every strategy category there are examples of measures that would require some legislation or rulemaking. The primary exception is Traffic Flow/Speed Measures, which typically enhance infrastructure and traffic management programs that are already within the jurisdiction of transportation agencies. Other measures not requiring legislation or rulemaking include individual measures that enhance infrastructure for transit, bicyclists, and pedestrians, and some measures that enhance infrastructure for goods movement. These measures do not inherently require legislation, unless legislation is needed to raise funding for implementation of the measures.
More than half of the sample strategies, but less than one third (29%) of estimated reductions, depend upon some new public funding. There are examples within almost every strategy type of measures that would require some funding, with the exception of strategies that are purely regulatory, with no pilot programs or incentives provided. These measures include new vehicle emissions standards and parking, road, and fuel pricing measures. Funding requirements tend to be greatest for the strategies that depend upon infrastructure investments. Every strategy involving Transit and Alternative Modes, Freight Systems Strategies, and Non-Road Measures requires some new public funding.
About one third of strategies and one quarter (25%) of tons reduced depend upon a major new initiative within a public agency or a major new planning process. All Smart Growth strategies would require some such initiative, as would most Transit and Alternative Modes and Freight Systems Strategies. Strategies that involve regulation of fuels or technologies generally do not require a major new government initiative.
More than a third of strategies and about one fifth (20%) of estimated emissions reductions would require states to promote or incentivize private industry collaboration through non-regulatory means. Incentivizing voluntary changes by private industry is often an alternative to a legal mandate that applies to private industry. Strategy types that do not require incentives for private industry tend to be technology and fuels regulations, including Clean Car Standards and Low Carbon Fuel Standards. In addition, LDV Clean Vehicle Purchase Incentives and Traffic Speed/Flow Measures do not rely on private industry collaboration.
Table 9 shows the sample states in terms of the amount of the GHG reductions subject to the different requirements for enactment. (The column sum is greater than the total because some strategies involve more than one requirement for enactment.) While all states rely heavily on strategies that would involve new legislation or rulemaking, there are differences for the other requirements. The GHG reductions in Oregon's CAP, for example, rely very little on new public funding, unlike other states. Washington's CAP depends heavily on major new public agency initiatives. And the Minnesota and South Carolina CAPs rely heavily on private industry collaboration.
| Public Funding | Legislation or Rulemaking | Major Public Agency Initiative | Private Industry Collaboration | Total | |
|---|---|---|---|---|---|
| California | 14.7 (24%) | 61.3 (98%) | 9.5 (15%) | 9 (14%) | 62.3 (100%) |
| Colorado | 1.1 (13%) | 8.6 (100%) | 1.4 (17%) | 2 (23%) | 8.6 (100%) |
| Iowa | 5.2 (47%) | 11.1 (100%) | 0.9 (8%) | 0.9 (8%) | 11.1 (100%) |
| Maine | 1.6 (49%) | 3.2 (100%) | 0.3 (9%) | 0.7 (22%) | 3.2 (100%) |
| Minnesota | 3.5 (34%) | 9.6 (93%) | 2.3 (22%) | 4.8 (46%) | 10.3 (100%) |
| New York | 2.4 (46%) | 4.4 (85%) | 1.3 (25%) | 1.6 (31%) | 5.2 (100%) |
| Oregon | 0.1 (2%) | 5.9 (99%) | 0.4 (6%) | 0.4 (7%) | 6 (100%) |
| South Carolina | 3.4 (39%) | 8.1 (94%) | 2.4 (27%) | 3.6 (41%) | 8.7 (100%) |
| Washington | 6.9 (36%) | 14.5 (75%) | 15.2 (79%) | 3.5 (18%) | 19.2 (100%) |
Once a strategy is enacted, a variety of external factors can influence the resulting GHG reductions. These factors are not quantified per se in the estimate of GHG reductions, and they are generally outside of the immediate control of the legislative bodies and agencies that can enact statewide measures. We identified four such external factors in our review:
We gave each measure a rating of High, Medium/Low, or Very Low/None for each factor. (A guide to the rating scheme is provided in the appendix to this report). Ratings indicate the level of potential for uncertainty in each category to prevent the measure from achieving the reduction estimated in the CAP. If, for example, achieving the estimated GHG reduction depends upon deployment of technologies not commercialized yet or not yet demonstrated at similar scale, we would score this strategy as "High" on commercial availability of technology. The ratings are used to distinguish the relative level of influence or uncertainty of each factor from strategy to strategy. Table 10 shows the strategies rated "high" on each implementation factor.
| No. of Strategies (% of total) | MMtCO2e Reduced (% of total) | |
|---|---|---|
| Commercial availability of technology | 2 (2%) | 1.2 (1%) |
| Local government action or coordination among government agencies | 19 (23%) | 28.8 (21%) |
| Market forces | 38 (45%) | 44.2 (33%) |
| Land use changes | 10 (12%) | 13.3 (10%) |
| All Strategies in Sample | 84 (100%) | 134.6 (100%) |
As the most broadly defined category, market forces are also the most broadly relevant. For nearly half of all strategies and one third (33%) of tons reduced, market forces (including transportation prices and housing prices), could have a major impact on the actual effects of strategies. These include strategies that work specifically by changing the price of transportation, including PAYD and Parking, Road, and Fuel Pricing strategies. If baseline fuel prices drop in response to fluctuations in energy markets, these strategies might produce fewer reductions than estimated. Transit and Alternative Modes measures are also highly subject to market fluctuations, since the prices of driving versus alternatives modes are a major factor in mode selection. Strategies that depend entirely on financial incentives to change vehicle purchasing patterns are also highly subject to market fluctuations. Finally, Smart Growth strategies can be disrupted by market forces, since achieving more infill, mixed-use, and transit-oriented development depends on the activities of private developers.
About one fifth of the strategies and emissions reductions projected are highly dependent on some local government action or coordination among government agencies. Every Smart Growth strategy falls into this category. In addition, most strategies that provide more infrastructure for transit, bicyclists, and pedestrians also require local government action.
About one tenth of strategies and tons reduced are highly dependent on land use changes. All of these tons are associated with Smart Growth strategies, in which land use changes are inherent.
Only two strategies in our sample are highly dependent on a technology that is not yet ready for large scale implementation. Both of these strategies depend upon large scale expansions of alternative propulsion technologies-plug-in hybrid-electric vehicles (PHEVs), in the case of one strategy, and biofuels in the other case.
In total, our sample includes 39 strategies and 44 tons of emissions reduced that we rated "high" in any category of external factors affecting implementation. This reflects 33% of the total tons of reduction in the sample. We consider these strategies to be the most likely to fall short of reductions estimated, if enacted as stated.
Quantification methods describe the basic approach that analysts used to estimate GHG reductions from each measure, as provided in CAPs. Quantification methods include:
Most strategies are quantified using a single method, but a few are quantified in separate parts, using more than one method. In a few cases, a single goal was set for emissions reductions from both VMT reduction and technology/operational changes. The method of quantification is generally evident from the text of strategies and descriptions of quantification. For those plans that provided no details of how reductions were estimated, we applied a best guess based on industry standard quantification methods. Where there is no evident standard method, we assumed that strategies were quantified using goals, with less specific technical calculations.
Table 11 below shows the distribution of strategies and tons across the various quantification methods. For goal-based strategies, the subset of strategies for which numerical goals are supported by a specific feasibility analysis are broken out.
| No. of Strategies (% of total) | MMtCO2e Reduced (% of total) | |
|---|---|---|
| VMT elasticities/impact study | 16 (19%) | 10.9 (8%) |
| VMT reduction goal | 17 (20%) | 26.2 (19%) |
| (supported by local feasibility analysis, outside CAP) | 5 (6%) | 9.9 (7%) |
| Technology/fuels/operational standard | 39 (46%) | 60 (45%) |
| Technology/fuels/operational GHG goal | 14 (17%) | 41 (30%) |
| (supported by specific feasibility analysis, outside CAP) | 3 (4%) | 18.5 (14%) |
| All Strategies in Sample | 84 (100%) | 134.6 (100%) |
( ) strategies are a subset of the preceding row |
Nearly half of all strategies and tons reduced are quantified by the technology/fuels/operational standard method. Most of the tons reduced in this category are associated with LDV New Vehicle Emissions Standards, including the California Pavley standards.
Nearly one fifth of strategies and one third of tons reduced are quantified using technology/fuels/ operational GHG goals. About half of those are supported by a specific feasibility analysis. Low Carbon Fuel Standards (LCFS) account for about half of the goal-based tons; these strategies are inherently goal-based. Only in California is the goal, a 10% reduction in carbon intensity of fuels by 2020, backed up by a local feasibility analysis. Other technology/fuel/operations strategies quantified using a GHG goal include several individual Traffic Speed/Flow Measures and some fleet-based measures.
Fewer strategies are quantified using VMT reduction goals and VMT elasticities/impact studies. VMT reduction goals are generally used for measures that include broad packages of investments and programs, where the individual effect of each one would be difficult or even impossible to separate. These include many Smart Growth strategies and Transit and Alternative Mode strategies. Strategies quantified using VMT elasticities or impact studies are generally those that provide specific investments or strategy changes.
We can broadly characterize the certainty of achieving estimated reductions based on the method used to analyze each strategy. Estimates produced using empirical data on the impacts of specific implementation measures can be fairly certain, if conservative assumptions are used. Most strategies have been evaluated using conservative assumptions. Some strategies may even produce more reductions than estimated. Strategies quantified by using VMT-based goals or goals related to shifts in technology, fuels, or operations can also be fairly certain if reinforced by a state-specific feasibility analysis. Such a feasibility analysis studies the potential for changes in VMT or reductions in emissions in accordance with specific strategies; these analyses go beyond the scope of the CAP process. Strategies quantified through the use of goals but not supported by feasibility analyses are the least likely to achieve reductions estimated. Twenty-two strategies accounting for 38.3 tons of reductions fall into this category, or 28% of the total GHG reduction in the sample.
Sources of uncertainty in quantified emission reductions can be traced to several variables in a typical calculation of emission reduction. A general formula for determining emission reduction is:
| Reduction in Year X | = | Effected Population (emissions or VMT subject to the strategy) | x | Market Penetration (% uptake of strategy or technology) | x | Effectiveness (% reduction in VMT or emissions from affected parties) | x | Timing Adjustment (portion occurring in yr X |
|---|
Uncertainty in any of the four components of the formula will contribute to overall uncertainty in the reduction estimate. Specifically, uncertainty can arise in:
We assigned each measure a rating of High, Medium/Low, or Very Low/None for each factor. (A guide to the rating scheme is provided in the appendix to this report). Ratings correspond to the level of uncertainty of each factor in regard to the ultimate impact of the strategy. We assigned ratings based on disclosure of uncertainty within the text of strategies and based on our knowledge of transportation planning, strategies, and technologies. The ratings are used to distinguish the relative level of influence or uncertainty of one factor from strategy to strategy. Where less information was available, we assumed greater uncertainty and/or based assumptions on similar strategies in other states. Table 12 below shows the strategies rated "high" for each source of uncertainty.
| No. of Strategies (% of total) | MMtCO2e Reduced (% of total) | |
|---|---|---|
| Effected population | 7 (8%) | 0.8 (1%) |
| Market penetration | 54 (64%) | 43.3 (32%) |
| Effectiveness | 14 (17%) | 17.3 (13%) |
| Timing adjustment | 23 (27%) | 27.6 (20%) |
| All Strategies in Sample | 84 (100%) | 134.6 (100%) |
Market penetration is a high source of uncertainty for nearly two thirds of strategies and about one third of tons reduced. Estimating market penetration of a technology, measure, or transportation option is generally the most challenging part of an analysis, except where penetration rates themselves would be legally mandated. Where the literature suggests a range of penetration rates, most CAPs have tended to assume the lower end of the range in order to produce conservative estimates of emissions reduced.
About one fifth of GHG reductions are subject to high uncertainty around the timing of implementation. These strategies are generally those that require large and costly capital investment programs, or significant overhauls of planning and decision-making processes. All of the Smart Growth strategies and all but one of the Transit and Alternate Modes strategies received a rating of high in this category, as did most of the Freight Systems strategies.
A small proportion (about one tenth) of tons reduced are subject to high uncertainty around the effectiveness that strategies would have on VMT or GHG emissions per mile or per hour. The effectiveness of most strategies and technologies has been studied and reported in the literature or in models to within a reasonable degree of accuracy. Strategy types still subject to significant uncertainty in this category include some Traffic Speed/Flow Measures. While the impact of changes in traffic flow and vehicle drive cycles can be modeled using existing tools, these tools require a large amount of data and very specific information on changes to the transportation system. This level of analysis has not been incorporated in state CAPs. Smart Growth strategies are also subject to a high degree of uncertainty surrounding effectiveness of development patterns on individual driving behavior. These relationships have been studied extensively, and sophisticated modeling tools are available to help predict the impact of specific changes in physical environments on VMT. But these models are also highly complex and data intensive. None have been applied to the specific strategies in state CAPs.
Very few strategies and tons are subject to high uncertainty about the effected population. Strategies in this category are typically those that would apply to a very small segment of the transportation market, for which baseline data is not well established. Traffic Speed/Flow Measures and HDV anti-idling measures fall into this category.
Our rating system has not established a quantitative degree of uncertainty. Neither has it established whether variation in any of the key variables would be likely to raise or lower emission reduction estimates. Still, we can generally expect strategies with more ratings of "high" to be less certain to achieve reduction estimates. In our sample of 84 strategies, 55 strategies (48 tons of GHGs reduced) received a rating of "high" in at least one category.
On the other hand, any forecast is inherently subject to some uncertainty. Only six strategies, accounting for about one quarter of estimated reductions, received ratings of very low/no uncertainty for all four calculation variables. These six strategies are all Low Carbon Fuel Standard (LCFS) strategies. LCFS strategies, if enacted as described in the CAPs, apply to a large market segment of vehicles and have a legally mandated market penetration, implementation schedule, and GHG impact. The only source of uncertainty for LCFS strategies, once passed into law, is if the achievement of the goal is technically infeasible within the mandated time window. In California, an independent analysis found that achievement of that state's LCFS is technically feasible. Other states have not conducted such analyses.
In order to better understand the uncertainties in strategy estimates in our nine sample CAPs, we examined four of the most common strategy types: Smart Growth strategies, LDV Emission Standards, Alternative/Low Carbon Fuel Standards, and Transit strategies.
All nine states in our sample, and nearly all states with a CAP, have a strategy recommendation related to smart growth. These recommendations typically call for the state to enact regulations that would encourage local governments to implement planning activities that reduce VMT. The strategy recommendations usually involve requirement for more coordinated planning and incentive programs to encourage the private sector to build more infill and transit-oriented projects. They may also involve restrictions on new development (e.g., policies akin to the urban growth boundary implemented by Metro in Portland, Oregon).
The GHG benefits of these strategy recommendations can vary widely, based on two major factors:
To quantify the impacts of these strategies, most states rely on regional land use scenario modeling exercises performed by MPOs. In our sample, California, Colorado, Minnesota, and Washington based their VMT reduction calculation on MPO modeling in those states, while Maine relied on modeling from other states. South Carolina's smart growth strategy states a goal of holding 2010 VMT per capita constant, without a land use modeling exercise; the quantification of GHG impacts flows directly from that assumption. Iowa assumed a shift in new development to high density (i.e., infill) census tracts, and quantified the VMT impacts of such a shift. Assumptions used in New York and Oregon are not available, but GHG quantification was likely based on similar MPO scenario modeling results.
States make different assumptions about the portion of statewide VMT that the smart growth strategy applies to: all statewide VMT, only urban VMT, or only light-duty VMT, as shown in Table 13. Table 13 also summarizes the basis for the calculation of VMT reduction.
| State | Portion of VMT to which strategy applies | Affect on VMT in applicable areas | Net effect on all VMT | Forecast year | Assumptions |
|---|---|---|---|---|---|
| CA | All VMT | -4% | -4% | 2020 | Based on review of MPO scenario modeling in CA, which found median 4% per capita VMT reduction over 10 years |
| CO | Light duty VMT only (92% of total VMT) | -2% | -2% | 2020 | Based on 2020 modeling for DRCOG region, which found 2% reduction in VMT from compact land use scenario |
| IA | All VMT | N/A | N/A | 2020 | Assumes 60% of new development occurs in highest density census tracts |
| ME | All VMT | -4% | -4% | 2020 | Based on regional scenario modeling in other places |
| MN | Urban VMT only (50% of total VMT) | -12% | -6% | 2025 | Based on 2030 scenario study, which found 12% VMT reduction for Twin Cities under compact land use scenario |
| NY | N/A | N/A | N/A | 2020 | Assumptions not available |
| OR | N/A | N/A | N/A | 2025 | Assumptions not available |
| SC | All VMT | -9% | -9% | 2020 | Based on stated strategy goal to hold 2010 VMT per capita constant |
| WA | Urban VMT only (71% of total VMT) | -7% | -5% | 2020 | Based on PSRC Vision 2040 modeling, which found a 7% VMT reduction by 2020 under the "Metropolitan Cities Alternative" |
To understand the uncertainty in these numbers, we need to consider two fundamental questions:
Nearly all the uncertainty is wrapped up in the first question. There is relatively little uncertainty in the second question, since the effect of land use changes on VMT has been studied extensively, and most states based their calculations on modeling by MPOs.
The likelihood that the envisioned land use changes will actually occur is highly uncertain, in part because it generally relies on new statewide policies and programs that would alter local government land use planning practices. In most states, there is strong resistance to any strategy that would restrict local government discretion over land use planning. Even California's landmark SB 375 explicitly states that local plans and local zoning do not need to be consistent with the required regional plans demonstrating GHG compliance. Over the last decade, during which nearly all state climate action plans were completed, we are not aware of any new significant state policies that change local government planning processes for the purposes of reducing transportation GHGs.
The likelihood that the envisioned land use changes will actually occur is also highly uncertain because it depends on the action of private developers, who are driven by overall market conditions as well as household and business preferences for development products. Uncertain market demand is particularly important given the relatively short forecast period for these strategies (10 - 15 years). There is considerable debate among smart growth proponents and researchers regarding the extent to which buyer tastes are shifting toward new homes that are smaller, closer to major destinations, and located in walkable communities, and the extent to which developers will respond to shifting tastes.
Approximately half the states with quantified CAPs have included strategy recommendations for the adoption of California's light-duty vehicle GHG standards (the so-called Pavley standards). California is the only U.S. state with authority to adopt vehicle emissions standards that differ from federal standards, and other states have the option of implementing the California standards. Because this strategy affects all light duty vehicle travel, and because it requires a significant reduction in tailpipe GHG emissions by 2020, these strategies can account for a large portion of the GHG benefits in some state CAPs.
As shown in Table 14, the relative benefits of this strategy vary widely among the six states in our sample that have included it. Since the U.S. EPA has now granted California the waiver necessary to implement the standard (on June 30, 2009), any significant uncertainty regarding policy adoption has been removed. The differences in these numbers is due to two major factors: (1) differences in assumptions about the baseline, and (2) differences in methods for quantifying the strategy impacts.
| Estimated GHG Reduction | Baseline reflects new CAFE stds? | ||
|---|---|---|---|
State | MMtCO2e | % of Total Transportation Emissions | |
| CA | 31.7 | 14.1% | No |
| CO | 3.4 | 9.4% | No |
| IA | 0.8 | 2.9% | Yes |
| ME | 0.9 | 7.4% | No |
| MN | 1.2 | 3.0% | Yes |
| NY | N/A | N/A | - |
| OR | 6.1 | 19.1% | No |
| SC | 1.1 | 2.6% | Yes |
| WA * | N/A | N/A | - |
* Washington had already taken steps to adopt the California Clean Car GHG standards at the time of the CAP process, so this strategy was not included as a recommendation in Washington's CAP.
Virtually every state CAP includes one or more strategies to promote greater use of alternative (low carbon) fuels. Some such strategies are focused on small niche markets (e.g., state vehicle fleets) or focused on improving alternative fuel infrastructure or production. These strategies generally have small GHG impacts.
A number of state CAPs, including six in our sample, contain a strategy for a low carbon fuel standard that would mandate a reduction in the amount of carbon per unit of energy, to be applied to all motor vehicle fuels. These strategies have relatively large GHG impacts, as shown in the table below.
For strategies like a LCFS that are stated in terms of life-cycle carbon impacts, the uncertainty in the GHG impact estimates come primarily from the uncertainty of policy adoption. To date, only California is in the process of implementing rules for a mandatory LCFS. Once adopted, the uncertainty in GHG estimate is relatively small, since the GHG outcome is directly tied to the strategy formulation.
Other state CAPs (such as Maine's) include strategies that would set goals for use of specified renewable fuels (ethanol, biodiesel). In these cases, the GHG impacts of the strategy have greater uncertainty because of the differences in life cycle GHG impacts of fuels and feedstocks. For example, corn ethanol has much lower GHG benefits than ethanol devised from woody biomass. Because these types of strategies are typically silent on the feedstock and required life cycle GHG impact, there is potential that fuels with minimal GHG benefits (like corn ethanol) could be used to satisfy the requirement.
| Estimated GHG Reduction | Key Assumptions | ||
|---|---|---|---|
| State | MMtCO2e | % of Total Transportation Emissions | |
| CA | 15.0 | 7% | LCFS - 10% reduction in fuel carbon intensity by 2020 |
| CO | 2.2 | 6% | modeled after CA LCFS |
| IA | 5.1 | 19% | double the goal of CA LCFS |
| ME | 0.6 | 5% | assumes 100% E-10 and B-5 |
| MN | 3.6 | 9% | modeled after CA LCFS |
| NY | N/A | N/A | N/A |
| OR | 1.0 | 3% | assumes 20% B20 |
| SC | 3.7 | 8% | modeled after CA LCFS |
| WA | 3.6 | 6% | modeled after CA LCFS |
Approximately half of state CAPs include a strategy recommendation to promote and expand public transit. In some states, these strategies have been bundled with related strategies to promote alternative travel modes, such as ridesharing, bicycle and pedestrian improvements, or land use strategies. This category also includes strategies focused on intercity rail service. The estimated GHG impact of these strategies is typically small (1% or less of total transportation sector emissions). But in a handful of states, the estimated GHG impacts are much larger (6 - 8% of transportation sector emissions).
The uncertainty in the GHG estimates of these strategies is due primarily to quantification methods. Most strategies assume a large increase in transit ridership (doubling by 2020 is typical). The methods used to estimate the GHG impacts of this increase vary widely. Differences in calculation methods include:
Table 16 illustrates the effects of these different calculation assumptions for the five states in our sample that have a strategy focused on urban public transit. In the states with the largest percent reductions, GHG benefits were calculated based on stated VMT reduction goals. Only two of the five examples calculated the increase in transit vehicle emissions.
| Estimated GHG Reduction | |||
|---|---|---|---|
| State | MMtCO2e | % of Total Transportation Emissions | Key Assumptions in Calculations |
| CA | N/A | - | |
| CO | 0.97 | 2.7% | Based on goal of reducing light-duty vehicle urban VMT by 6%, which comes from literature; does not calculate increase in transit vehicle emissions |
| IA | 0.03 | 0.1% | Based on increase in transit service and frequency; ridership changes based on published elasticities; assumes mode shift factor of 1; includes increase in transit vehicle emissions |
| ME | N/A | - | |
| MN | 0.3 | 0.8% | Based on doubling transit ridership; assumes mode shift factor of 1; does not calculate increase in transit vehicle emissions |
| NY | N/A | - | |
| OR | N/A | - | |
| SC | 0.02 | 0.0% | Based on increase in transit service and ridership; assumes mode shift factor of 1; does not calculate increase in transit vehicle emissions |
| WA | 3.6 | 6.3% | Based on VMT reduction goals for individual transit and ridesharing programs; includes increase in transit vehicle emissions |
Our detailed review of 84 strategies in nine CAPs reveals the difficulties with generalizing about likelihood that states will actually achieve the transportation GHG reductions estimated in their CAPs. The nine plans we reviewed estimate a total GHG reduction of 134.6 MMtCO2e. It is impossible for us to say what fraction of that total is "likely" to occur within the analysis time frame. The total certainty of achieving reductions depends upon each of the characteristics discussed in this section. In order to actually achieve the GHG reductions estimated in CAPs, strategies must:
Our detailed review of a sample of CAPs does shed light the major factors affecting emission reduction certainty. Key findings include the following:
Overall, much of the uncertainty in state CAPs is related to the political challenges of adopting new policies and regulations. Once a policy is enacted, then the GHG reduction uncertainty is more related to analytical issues. In order to draw some general conclusions, we can consider three different ways to broadly judge the certainty that enacted policies will achieve at least the reductions estimated:
In our sample, estimates of tons that fall into each of these three categories range from 29 to 48 MMtCO2e, or from 21% to 36% of total reductions estimated. We therefore judge that, if state CAP transportation strategies are enacted as stated, roughly one third of the GHG reduction is highly uncertain. Conversely, roughly two thirds of the CAP strategies are likely to achieve at least their estimated GHG reductions, and possibly more.
Guide to ratings for External Implementation Factors.
Guide to ratings for Calculation Variables.
[2] N/A: Not available. Totals accounting for overlaps are calculated as the effect of simultaneously implementing all strategies in a state plan, which is less than the sum of individual strategies.
[3] N/A: Not available. Figures less than 0.5% are rounded to 0%. No figures are provided for CT, ME, and NH, RI, and WI, because no transportation GHG forecast was readily available for those states.
