@article{30660,
  author = {Hanna Breunig and Tyler Huntington and Ling Jin and Alastair Robinson and Corinne D Scown},
  title = {Dynamic Geospatial Modeling of the Building Stock To Project Urban Energy Demand},
  abstract = {<p>In the United States, buildings account for more than 40% of total energy consumption and the evolution of the urban form will impact the effectiveness of strategies to reduce energy use and mitigate emissions. This paper presents a broadly applicable approach for modeling future commercial, residential, and industrial floorspace, thermal consumption (heating and cooling), and associated GHG emissions at the tax assessor land parcel level. The approach accounts for changing building standards and retrofitting, climate change, and trends in housing and industry. We demonstrate the automated workflow for California and project building stock, thermal energy consumption, and associated GHG emissions out to 2050. Our results suggest that if buildings in California have long lifespans, and minimal energy efficiency improvements compared to building codes reflective of 2008, then the state will face a 20% or higher increase in thermal energy consumption by 2050. Baseline annual GHG emissions associated with thermal energy consumption in the modeled building stock in 2016 is 34% below 1990 levels (110 Mt CO<sub>2eq</sub>/y). While the 2020 targets for the reduction of GHG emissions set by the California Senate Bill 350 have already been met, none of our scenarios achieve &gt;80% reduction from 1990 levels by 2050, despite assuming an 86% reduction in electricity carbon intensity in our “Low Carbon” scenario. The results highlight the challenge California faces in meeting its new energy efficiency targets unless the State’s building stock undergoes timely and strategic turnover, paired with deep retrofitting of existing buildings and natural gas equipment.</p>},
  year = {2018},
  journal = {Environmental Science & Technology},
  volume = {52},
  pages = {7604 - 7613},
  month = {06/2018},
  issn = {0013-936X},
  doi = {10.1021/acs.est.8b00435},
  language = {eng},
}