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No More Holes: Estimating Building Data

Binary TowersWhen calculating a carbon inventory, or reporting on tenant-level energy use, there’s no substitute for actual, raw data. That data can live in many formats and reside at any number of primary or middle level repositories such as utility companies, bill pay providers or any manner of private service providers. To ensure our clients can easily access their data, I’ve been building programming interfaces that allow our users, with the click of a button, to access their data from a huge variety of sources and import it into Measurabl. So far, we’ve wired up more than 800 utility companies throughout the country, the U.S Government’s ENERGY STAR Portfolio Manager, and a variety of building data management systems and bill pay providers.

But no matter how extensive our data import capabilities, the simple truth remains: many of our customers will never be able compile their sustainability reports based exclusively on actual data. Sometimes the necessary information lies upstream with suppliers. Sometimes they simply don’t pay the utility bills but are still on the hook to report the information—a common case for our real estate customers reporting to GRESB. Fortunately, carbon accounting protocols like the WRI/WBCSD GHG Protocol or the Climate Registry’s General Reporting Protocol have long and robust methodologies for properly estimating emissions when actual data is incomplete or unavailable. The bottom line: lack of data is not an excuse to take a pass on sustainability reporting, or leave your report with so many blanks that it looks like it was used for target practice. Properly applied, estimates can be incredibly accurate and provide a meaningful understanding of your performance.

Thanks to our friend Robbie Adler, Co-Founder & President of customer acquisition start-up Faraday, we’ve begun installing and fine tuning a set of APIs (Application Programming Interfaces) that deliver accurate and responsive carbon estimates to help fill holes in our users’ reports. My next step is to write estimation algorithms that step in when our users have no data at all. The goal: supplement data to achieve more complete reporting and make sure incomplete data no longer stops reporters before they start.

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