Travel and emissions models are commonly applied to evaluate the change in passenger and commercial travel and associated greenhouse gas (GHG) emissions from land use and transportation plans. Analyses conducted by the Sacramento Area Council of Governments predict a decline in GHGs from their land use and transportation plan (PRB) relative to the “Business-As-Usual” (BAU). An earlier study conducted by the authors, applied a spatial economic model (Sacramento PECAS) to the PRB plan and found that lower labor, transport, and rental costs increased producer and consumer surplus and production and consumption relative to the BAU. As a result, lifecycle GHG emissions from these upstream economic activities may increase. At the same time, lifecycle GHG emissions associated with the manufacture of construction materials for housing may decline due to a shift in the plan from larger luxury homes to smaller multi-family homes in the plan. To explore the net impact of these opposing GHG impacts, the current study used the economic production and consumption data from the PRB and BAU scenarios as simulated with the Sacramento PECAS model as inputs to estimate the change in lifecycle GHG emissions. The economic input-output lifecycle assessment model is applied to evaluate effects related to changes in economic production and consumption as well as housing construction.

This study also builds on the findings from two previous studies, which suggest potential economic incentives for jurisdictional non-compliance with Sustainable Communities Strategies (SCSs) under Senate Bill 375 (also known as the “anti-sprawl” bill). SB 375 does not require local governments to adopt general plans that are consistent with the land use plans included in SCSs, and thus such incentives could jeopardize implementation of SCSs and achievement of GHG goals. In this study, a set of scenarios is simulated with the Sacramento PECAS model, in which multiple jurisdictions partially pursue the BAU at differing rates. The PRB is treated as a straw or example SCS. The scenarios are evaluated to understand how non-conformity may influence the supply of housing by type, and holding other factors constant, the geographic and income distribution of rents, wages, commute costs, and consumer surplus.