An Interactive Dashboard: Building Energy Efficiency Justice Analysis in New York City

Climate change is one of the greatest threats to New York City. Continued rising heat, sea level rise, and extreme weather times threaten urban residents, communities, and our built environment for the foreseeable future. Buildings account for two-thirds of New York City’s GHG emissions. A large portion of the buildings in NYC are already built. This reality makes retrofitting existing buildings critical important and improving the energy efficiency of existing buildings becomes critically promising. New York City has introduced a number of laws and programs in place to improve building energy efficiency. The energy efficiency rating is one of the most direct and also one of the most widely used indicators of building energy efficiency, which is used to evaluate building's energy consumption, overall comfort, and energy costs. The research question for this capstone is the characteristics of Energy Star Score in terms of their distribution in time and space. This capstone first uses statistical methods to analyze the distribution of Energy Star Score by timeline, building type, and other factors. Then, linear regression analyzed the relationship between Energy Star Score and building-related factors. Finally, the Multi-Criteria Decision Analysis method is used to analyze the Energy Star Score geographically. The main goal of this Town + Gown capstone is to examine the performance of building energy efficiency across New York City based on this Energy Star Score through spatial analysis and regression analysis, and also identify areas where low levels of building energy efficiency intersect with environmental justice indicators such as low-income population and non-white population. T he primary output of the capstone is a platform with visual interactive energy efficiency maps and a straightforward dashboard that can be easily manipulated to display visual statistical and spatial data. The deliverables of this capstone are intended to serve the decision-making needs of relevant NYC agencies and other stakeholders, such as environmental justice groups, landlords, investors in building retrofits and community advocates. Firstly, this capstone uses regression model to analyze the factors associated with Energy Star Score and found that low-income population and non-white population were associated with Energy Star Score. Next, using the Environmental Justice Index cites, this capstone calculates the Building Energy Efficiency Justice (BEEJ) index at the census tract level, combining low-income population and non-white population. Secondly, this capstone uses the Multiple Criteria Decision Analysis (MCDA) method to refine the BEEJ scores that may be inflated by combining the number of D-graded buildings. Finally, this capstone created an interactive dashboard using statistical and spatial data built on the GitHub platform and written using HTML, CSS, and JavaScript. The dashboard first uses a scrolling map to provide a spatial narrative story about Energy Star Score and Building Energy Efficiency Justice (BEEJ) scores for any audience unfamiliar with Energy Star Score. Next, the dashboard uses Python and PostgreSQL for data analysis and builds an interactive data dashboard through the Tableau platform. The dashboard combines spatial and statistical data to provide stakeholders with intuitive data visualization. Finally, the dashboard develops interactive maps that provide spatial visualization of BEEJ and MCDA maps. The dashboard's full database is updatable and publicly available for download in preparation for possible future research purposes. The dashboard link is listed below: https://browndby366.github.io/FirstGit/Capstone/landing/index.htm