Forecasting greenhouse gas emissions from urban regions: microsimulation of land use and transport patterns in Austin, Texas

Abstract

Policymakers, planners, engineers, and others seek effective ways to anticipate and manage greenhouse gas (GHG) emissions for a sustainable future. Here, a microsimulation model was developed to demonstrate how one can forecast Austin’s demographic and firmographic attributes over time, using a variety of national and local, aggregate and disaggregate data sets. Year 2030 household energy demands and GHG emissions estimates are compared under five different land use and transport policy scenarios. Application of an urban growth boundary provided the lowest increase in overall vehicle miles traveled (VMT) and GHG emissions, while network additions resulted in the highest rates of increase. Average energy consumption per household are estimated to fall over time (by 11–19% depending on the scenario), but the region’s overall energy consumption is estimated to increase dramatically – by nearly 88% in terms of home energy consumption (in the base scenario) and 108% in the transport sector, relative to the 2005 base-year scenario. Such increases are considerably higher than proposed GHG targets, presenting a serious energy and emissions challenge for Austin as well as other U.S. regions.