Implications for the DC Region of California Senate Bill 743
It’s a timeless observation in business and government that you get what you measure. If you’re focused on congestion reduction, you may get transit facilities in the medians of highways. If you want apples-to-apples comparisons of student achievement, you may get curricula that prioritize teaching to the test. What happens, though, when you require planners to measure greenhouse gas emissions as an indicator of project environmental impact?
California is in the middle of this experiment, and because it demonstrates the importance of what we measure on what outcomes we actually get, it’s one the DC region should pay attention to.
Greenhouse Gas Emissions (GHG) and Vehicle Level of Service (LOS) in California
On September 27, 2013, California’s Governor Jerry Brown signed Senate Bill 743 into law. This bill is completely changing the way transportation impacts under the California Environmental Quality Act (CEQA) are analyzed, by encouraging infill development, multimodal networks, and greenhouse reduction.
Until recently, environmental review of transportation impacts in California – as in the Washington, DC region and the rest of the nation – focused mostly on the delay that vehicles experience at intersections and along roads. This vehicle delay is most often presented as “level of service,” or LOS, and is scored from “A” (no significant delay) to “F” (significant delay).
“Mitigating” the increases in vehicle delay that can occur when mixed use and TOD projects are built has traditionally involved increasing the size of roads to maintain “levels of service” for automobiles.
But in California, traditional approaches to measuring and reducing transportation impacts of land use projects conflict with the goals of another state law, Assembly Bill 32, enacted in 2006 to reduce California’s greenhouse gas (GHG) emissions to 1990 levels by the year 2020.
The reason that measuring LOS is inconsistent with AB 32 is that the mitigations for increased auto congestion tend to increase auto use and emissions, and to discourage alternative forms of transportation.
Now looking ahead, under SB 743, the focus of transportation-related environmental impact analysis under CEQA will shift from reducing driver delay (vehicle LOS) to reducing greenhouse gas emissions, an outcome that multimodal networks, and mixes of walkable land uses tend to promote.
What Happens When You Change Goals
The enactments of AB 32 and SB 743 have interesting ramifications that go beyond just GHG measurement and reduction goals.
Because the assessments of project impact under CEQA are legally challengeable, the agencies responsible for determining project impacts need to have legally defensible traffic and transportation models that measure GHG and the amount of auto travel per capita, etc.
The requirement for legally defensible tools means that the models and data used to analyze impacts need to be demonstrably good at predicting not only automobile travel, but all of the other ways that people may travel that traditional, auto-oriented forecasting tools have tended to neglect.
As a result, a new generation of transportation models has emerged that is better informed by data about how people behave in walkable, transit-oriented, mixed-use environments. New tools reflect TOD design elements such as street “griddedness,” “bike and pedestrian path connectivity,” path directness, etc. in regional transit forecasts, based on statistical analysis of transit ridership and how people get to transit stations in different kinds of development settings.
These improvements greatly reduce error rates in estimating transit ridership and in estimating how people will get to transit stations (for example, by driving and parking, being dropped off, or by walking/biking). For example, one model has improved station ridership estimates for 18 out of 23 Caltrain stations and for 80% of BART stations. The graphics below illustrate this visually.
Transportation Demand Management - Better Predictions of Impact, and More Enforceable with Big Data
New transportation forecasting tools have combined with developments in big data analysis to increase the effectiveness of transportation demand management (TDM) as a way to reduce GHG emissions.
This is because on the one hand forecasting tools have become more sensitive to the role of travel demand management tools…and on the other hand because big data tools make it easier to monitor performance in real time without the need for costly or irregular surveys.
If you have a “No net new peak-hour trip goal hour” trip goal, as Stanford University does, big data tools that track where people are coming from to campus and by what routes and modes they do so, are key tools to help compare current GHG emissions to 1990 levels – and already by 2008 they were LESS THAN.
Parking permits for employees and students peaked in 2004 at 14,800 for a campus population of 32,000. By 2010 the number of permits had declined to 12,300.
The percentage of employees commuting in single-occupant vehicles dropped from 72 percent in 2002 to 42 percent in 2013, and is 39 percent for all university commuters, including students, today.
Many other corporate and educational campuses in California have agreed to car trip reduction goals like Stanford’s as a condition of local development approvals. The ability to accurately predict and regularly monitor performance makes TDM programs more appealing as a planning policy tool.
Raising Money for Transportation Projects
To advance the State’s AB 32 goals of reducing GHG emissions to 1990 levels by 2020, California established a cap and trade system that requires producers of GHG gases to purchase State-issued rights to emit gases.
Until recently, only large industrial firms were required to purchase cap and trade permits/credits.
But on January 1st of this year, motor fuel distributors were also required to participate in the purchasing of permits/credits, and the first auction in February generated $970 million in revenue to the State, 25% of which has been allocated by the legislature to the California High Speed Rail project.
Fuel distributors are expected to pass on this cost to motorists, who will likely pay about $0.10 more per gallon as a result, and $0.14 per gallon by 2020.
Based on revenue projections updated to reflect the February auction, the California HSR project can expect to receive almost $1 billion in revenues over the next two years, twice as much as forecast in the Governor’s budget.
While the political fragmentation of the Washington, DC region makes the adoption of this kind of cap-and-trade funding mechanism unlikely, it is still instructive as an example for policy makers in our region to be aware of.