Energy innovation in the US buildings sector: Setting the stage and mapping the future
Description
An interactive dashboard for exploring the segmentation data from this study is available at: https://public.tableau.com/app/profile/ericjhwilson/viz/Segmentationflexibledashboard/About This data record provides the raw segmentation estimates, expert feedback frame and responses for the paper of the same name, which is summarized below. Paper summary: Buildings are a critical resource for managing emerging challenges in global energy systems, including accelerating load growth and rapid changes in the energy generation mix. However, the diversity of building energy end uses and stakeholders suggests many possible strategic opportunities for decision makers to pursue, and a comprehensive framework for comparing buildings sector innovation pathways does not exist. Using the United States as a case study, we develop the basis for such a framework by estimating all current and expected future sources of US building energy use, energy costs, and greenhouse gas emissions through 2050 under multiple scenarios for buildings sector development. Pairing these data with expert feedback, we identify key technical innovation pathways for the buildings sector, discuss implementation barriers for these pathways, and identify the actions that could address such barriers. The analysis distills focus areas for buildings sector planning and sets a common foundation for assessing the role of buildings in the broader transformation of national energy systems.
Files
Steps to reproduce
To reproduce the segmentation estimates for operational energy and fugitive emissions: 1) Download the version of Scout that was used in this analysis: https://github.com/trynthink/scout/releases/tag/pathways-v1.0.0. 2) Follow the Scout Installation Guide to configure the programs required for Scout to run: https://scout-bto.readthedocs.io/en/latest/installation_guide.html#install-guide. 3) Follow the set of instructions in "Scout_Run_Commands.xlsx," which is included in this record. Refer to the column "Output Generated" to determine which type of output each set of commands is generating. Ensure that results are moved to the folder that contains the postprocessing script, "post.py," which is also included in this record. 4) Run post-processing script "post.py" and refer to the segmentation data, which will be written to the file "Seg_Results.xlsx". These data are consistent with those in the "Scout output" tab of "Final_Segmentation_Data.xlsx".* * District heating and EV segment data were added manually on top of the Scout data (tab "manual additions") and thermal load data were drawn from separate studies ("thermal load attributions" tab). Estimates of embodied emissions were also separately generated based on an approach that is described further in the paper.
Institutions
- E O Lawrence Berkeley National Laboratory
- National Renewable Energy Laboratory