Smart Growth Policy as an Offset in GHG Cap-and-Trade

Therese Langer, American Council for an Energy-Efficient Economy

Keywords

Abstract

Smart growth measures can provide substantial reductions in greenhouse gas (GHG) emissions, but costs and benefits of such measures may be difficult to define and quantify. Even assuming adequate quantification, it is not at all clear that putting a price tag on GHG emissions would provide a substantial incentive to get smart growth programs off the ground. Furthermore, the primary means discussed at present of attaching such a price tag is setting up cap-and-trade programs for GHG emissions, and these programs so far have been designed only for power generators and, in some cases, large industrial emitters.

Despite these potential obstacles, we attempt to identify a smart growth measure that could receive credits under a cap-and-trade scheme, in order to consider the obstacles cited in a concrete context. A strong candidate measure will need to be quantifiable, verifiable, and costcompetitive, among other things. Even today, with no GHG cap-and-trade programs in place that cover the transportation sector, devising a smart growth measure that meets these criteria would in fact have practical implications, because the measure could be proposed as an offset for the Regional Greenhouse Gas Initiative (RGGI), another domestic agreement, or an international one.

The analytical underpinning for this effort is the substantial literature documenting the relationship between vehicle miles traveled (VMT) (and therefore GHG emissions) and various community design features such as density, proximity to transit, and walkability. These relationships could allow an existing smart growth zoning program to go far in establishing eligibility for credits in a cap-and-trade program, though some issues remain. These credits would not be sufficient to cover the costs of the program today, but under plausible future scenarios the credits could finance a substantial percentage of program costs.

ACEEE thanks the Surdna Foundation for its support of this work.