Because buildings are one of the largest contributors to greenhouse gas emissions, decarbonizing buildings is a critical front in the battle against climate change. Important steps include improving energy efficiency, adopting renewable energy sources, and promoting sustainable building practices, steps that will allow the US to reduce greenhouse gas emissions while also lowering energy costs, enhancing resilience, and creating healthier and more comfortable living and working environments.
Decarbonizing buildings is challenging because of multiple regulatory frameworks and the necessity to address both existing buildings through retrofits and new construction through building codes and appliance standards.
The Environmental Impact Data Collaborative (EIDC) is partnering with BlocPower, a climate technology company based in Brooklyn, NY to provide data on the U.S building stock, data that can be useful to design and assess decarbonization strategies and policies. Through this collaboration, we are able to provide access to energy equipment and consumption information for over 121 million buildings in EIDC’s data platform. The data is sourced in part from tax assessment records, which furnish details on building system types and attributes such as construction year, building area, and many others. This data is then utilized as inputs for the Automatic Building Energy Modeling (AutoBEM) developed by Oak Ridge National Laboratory. The AutoBEM generates modeled estimates of building energy consumption. To provide insights into building decarbonization at the city and local levels, BlocPower employs cloud-based building-level data pipelines to deploy their software-as-a-service (SaaS) solution known as BlocMaps.
The goal of this project is to enable users to analyze energy usage patterns in buildings across the United States in ways that support decarbonization or other efforts to fight climate change or to advance environmental justice.
The energy use intensity (EUI) of a building measures the energy efficiency of a building. It is used to assess how efficiently a building utilizes energy resources relative to its size. It represents the amount of energy consumed per unit of floor area over a given period.
The map above illustrates the distribution of average energy use intensity across all states (interactive version gives a look at counties as well via drilldown) in the United States.
Several intriguing findings emerge from this analysis. Firstly, states with the lowest average energy use intensity frequently exhibit a consistent pattern where in a few counties stand out as outliers with remarkably high average energy use intensity. This trend is observed across various states. For example, in Texas, counties Jeff Davis and Sabine deviate from the overall trend of low energy consumption. Similarly, California experiences this disparity with counties like Trinity and Alpine, while Florida has multiple outlier counties such as Holmes, Liberty, Madison, and Columbia, significantly skewing the state's average towards higher values. Notably, the higher average energy usage intensity in Florida is primarily concentrated in its Northwest region, indicating the potential for valuable insights upon further investigation.
Determining the precise reasons behind the variations in average energy usage intensity among countries requires further investigation. Without conducting a detailed analysis, it is challenging to identify the specific factors that contribute to higher energy usage intensity in certain countries compared to others.
BlocPower independently developed models that effectively identify buildings with high,medium and low energy potential. The energy potential of a building refers to its capacity or opportunity to achieve substantial energy savings or efficiency enhancements. The outcomes generated by their models are presented below:
Several intriguing findings emerge from this analysis. Firstly, states with the lowest average energy use intensity frequently exhibit a consistent pattern where in a few counties stand out as outliers with remarkably high average energy use intensity. This trend is observed across various states. For example, in Texas, counties Jeff Davis and Sabine deviate from the overall trend of low energy consumption. Similarly, California experiences this disparity with counties like Trinity and Alpine, while Florida has multiple outlier counties such as Holmes, Liberty, Madison, and Columbia, significantly skewing the state's average towards higher values. Notably, the higher average energy usage intensity in Florida is primarily concentrated in its Northwest region, indicating the potential for valuable insights upon further investigation.
Determining the precise reasons behind the variations in average energy usage intensity among countries requires further investigation. Without conducting a detailed analysis, it is challenging to identify the specific factors that contribute to higher energy usage intensity in certain countries compared to others.
BlocPower independently developed models that effectively identify buildings with high,medium and low energy potential. The energy potential of a building refers to its capacity or opportunity to achieve substantial energy savings or efficiency enhancements. The outcomes generated by their models are presented below:
The analysis of the provided plot reveals several notable insights. Firstly, it is evident that Kansas stands out as the state with counties exhibiting the highest percentage of buildings with significant energy potential. This finding suggests a concentrated effort towards energy-efficient infrastructure in these regions. Secondly, a majority of states demonstrate a balanced distribution of counties, encompassing both buildings with high and low energy potential. This equilibrium indicates a combination of energy efficiency practices and infrastructure across the nation. However, it is important to acknowledge the presence of numerous counties where no buildings are classified as having high energy potential. This discrepancy may be attributed to potential data quality issues that necessitate further investigation.
The drill down plot above focuses on showing the zip codes with the highest average energy use intensity for the 10 states with the highest value in that category. Although most zip codes appear to have similar average energy use intensities, there is a notable difference between states. States with lower average energy use intensities tend to have a wider variation among zip codes. For instance, zip codes in Vermont (VT) have a narrower range of average energy usage intensity compared to zip codes in Tennessee (TN).
In the context of this study, disadvantaged communities were defined by Climate and Economic Justice Screening Tool (CEJST) in the following manner (quoted from their official website):
“As set forth in Executive Order (EO) 14008 on Tackling the Climate Crisis at Home and Abroad, disadvantaged communities are those that are marginalized, underserved, and overburdened by pollution. In line with this Executive Order, the beta version of the Climate and Economic Justice Screening Tool (CEJST) uses a methodology and datasets that identify communities that are economically disadvantaged and overburdened by pollution and underinvestment in housing, transportation, water and wastewater infrastructure, and health care. A community qualifies as “disadvantaged” if the census tract is above the threshold for one or more environmental or climate indicators and the tract is above the threshold for the socioeconomic indicators.”
The presented plot focuses on identifying zip codes in Kentucky where more than 50% of buildings are classified as having medium/high energy potential and where disadvantaged communities make up more than 30% of the population. It's important to note that this analysis can be replicated using different thresholds and applied to various states. By adjusting the thresholds, researchers can explore different combinations of energy potential and disadvantaged community representation, allowing for a more comprehensive examination of energy efficiency opportunities and social equity considerations across different regions and states.
The uniquely granular nature of this dataset that’s available in the data collaborative can be leveraged to create detailed energy source and destination diagrams for heating fuel/system types. The interactive Sankey plot provided below explores energy usage patterns across the United States in relation to different fuel types. It is worth noting that the plot excludes states with missing data (states with missing data: TX, FL, GA, DC, DE, MD, NC, SC, and WV). This exclusion ensures that the analysis focuses on regions where complete data is available, allowing for a more accurate and insightful examination of energy consumption patterns and trends across the country.
Buildings that rely on a combination of gas as a fuel type and forced air systems account for a significant portion of the overall site energy consumption in the United States. The identification of these buildings is an essential initial step to identify and capitalize on opportunities to improve energy efficiency. By recognizing and targeting these specific buildings, it is possible to focus efforts on implementing strategies and technologies that can lead to substantial energy savings and promote more sustainable practices in the built environment.
Addressing the issue of building efficiency in the United States is a multifaceted and intricate undertaking. Our goal in releasing this data is to provide data that can help us understand the state of affairs in building decarbonization. The data is evolving; we hope to work with partners to provide, understand and visualize the data in ways that will support effective policy making.