Simulation of the GHG Abatement Potentials in the U.S. Building Sector by 2050

Given the substantial contribution of the U.S. building sector to national carbon emissions, it is clear that to address properly the issue of climate change, one must first consider innovative approaches to understanding and encouraging the introduction of new, low-carbon technologies to both the commercial and residential building markets. This is the motivation behind the development of the Stochastic Lite Building Module (SLBM), a long range, open source model to forecast the impact of policy decisions and consumer behavior on the market penetration of both existing and emerging building technologies and the resulting carbon savings. The SLBM, developed at Lawrence Berkeley National Laboratory (LBNL), is part of the Stochastic Energy Deployment System (SEDS) project, a multi-laboratory effort undertaken in conjunction with the National Renewable Energy Laboratory (NREL), Pacific Northwest National Laboratory (PNNL), Argonne National Laboratory (ANL) and private companies. The primary purpose of SEDS is to track the performance of different U.S. Department of Energy (USDOE) Research and Development (R&D) activities on technology adoption, overall energy efficiency, and CO{sub 2} reductions throughout the whole of the U.S. economy. The tool is fundamentally an engineering-economic model with a number of characteristics to distinguish it from existing energy forecasting models. SEDS has been written explicitly to incorporate uncertainty in its inputs leading to uncertainty bounds on the subsequent forecasts. It considers also passive building systems and their interactions with other building service enduses, including the cost savings for heating, cooling, and lighting due to different building shell/window options. Such savings can be compared with investments costs in order to model real-world consumer behavior and forecast adoption rates. The core objective of this paper is to report on the new window and shell features of SLBM and to show the implications of various USDOE research funding scenarios on the adoption of these and other building energy technologies. The results demonstrate that passive technologies contain significant potential for carbon reductions - exceeding 1165 Mt cumulative savings between 2005 and 2050 (with 50% likelihood) and outperforming similar R&D funding programs for distributed photovoltaics and high efficiency solid-state lighting