Thursday, June 15, 2017

Carbon Group Post 2: What About the Data?

Carbon Group Post 2: What About the Data?

This is the second in a planned series of blog posts on topics that are discussed in depth in the SEI Sustainability Committee’s forthcoming technical report, Building Structure and Global Climate, due out later this year.

As with all analyses, the quality of environmental assessment results depends greatly on the quality of the input data. What we learned in our earliest structural engineering and computer programming courses holds true: garbage in = garbage out. When it comes to environmental analyses of our structures, no matter the level of detail of the analysis—carbon dioxide equivalent footprint (carbon footprint), life-cycle inventory, or life-cycle analysis (LCA)—quality input data is important to achieve credible, verifiable, and consistent results.

Uncertainty in analyses related to data quality and variability is important enough that an entire chapter of the SEI Sustainability Committee’s forthcoming technical report is devoted to the topic. In this post, I’ll highlight the key questions to ask related to data when evaluating a carbon footprint.

Data quality and variability
When performing a carbon footprint, data typically can be taken from a few different types of sources:
·         Public databases
·         Private databases
·         Primary data
A public database is usually managed by an association or a public or private institution. In the U.S., one public database is the U.S. LCI database and it is managed by NREL. Private databases can belong to large consulting firms that have performed multiple LCAs or software companies. Lastly, primary data can be obtained directly from product manufacturers. These data are the most applicable to the analysis, yet they are typically the most time consuming and expensive to get. In all cases, data for use in carbon footprint studies includes quantities of materials and energy that go into the system boundary being studied, and emissions to land, water, and air that come out of the system boundary.

Regardless of the source, data used in carbon footprints should be accompanied by evaluations of data quality and variability. Unfortunately, most LCI datasets do not include statistical variability. Information such as standard deviation and statistical distribution can be useful when evaluating or comparing carbon footprints. When evaluating an LCA report or EPD, research whether average data from public or private databases is representative of the industry. Keep relative uncertainty and statistical significance in mind when using data and results. Another way to check reliability of results is to request uncertainty/variability information from suppliers and trade organizations.

Data quality guidance is given in ISO standard 14044, Environmental Management-Life Cycle Assessment-Requirements and Guidelines. Typical factors that are addressed include: the timeliness, geographic and technical relevance, precision, completeness, representativeness, consistency, reproducibility, sources, and uncertainty of the information (for example, data, models, and assumptions).

This brief introduction to data quality and variability for carbon footprints will hopefully give you some indicators to look for the next time you read one of these studies. 

Leave a Reply

Please leave us your comments.

SEI Sustainability Committee © 2011 & Main Blogger. Supported by Makeityourring Diamond Engagement Rings

You can add link or short description here