Thursday, July 20, 2017

Carbon Group Post 3: Example Demonstrating How SCMs Can Reduce Embodied Impacts of a Concrete Building

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This is the third 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.

In this example, a cradle-to-gate LCA was conducted to determine the embodied impacts of concrete on a building to compare the Global Warming Potential (GWP) of a reference building using typical concrete mixes with moderate amounts of Supplementary Cementitious Materials (SCMs) such as fly ash and slag and a proposed building using concrete mixes that have relatively high volumes of fly ash and slag. The building is an 18 story residential tower located in the northeast United States. Compressive strengths for each structural element are identified in Figure 1.




Figure 1. Specified compressive strength of concrete for an 18 story residential tower.

The first step in the analysis is to identify typical concrete mixes for the reference building. In 2014 (updated in 2016), National Ready Mixed Concrete Association (NRMCA) published benchmark mix designs and their environmental impacts for eight different regions in the United States (www.nrmca.org/sustainability). This example uses the benchmark mix designs for the Northeast region. 
The next step is to estimate mix designs that have significantly lower GWP than the benchmark mixes that still meet the performance criteria (strength, durability, etc.). This example uses high volume SCM mixes from the NRMCA Industry-Wide Environmental Product Declaration (EPD). A summary of the concretes selected for the reference and proposed building are provided in Tables 1 and 2.

Table 1. Mix designs selected for the reference building (from NRMCA benchmark report)

Concrete Element
Specified Compressive Strength (psi)
Portland Cement (lbs/yd3)
Slag
(lbs/yd3)
Fly Ash
(lbs/yd3)
SCM content
Mat Foundation
6000
782
119
82
20%
Basement Walls
5000
741
112
78
20%
Floors B2-1
5000
741
112
78
20%
Floors 2-18
5000
741
112
78
20%
Shear Walls
6000
782
119
82
20%
Columns
8000
967
147
102
20%

Table 2. Mix designs selected for the proposed building

Concrete Element
Specified Compressive Strength (psi)
Portland Cement (lbs/yd3)
Slag
(lbs/yd3)
Fly Ash
(lbs/yd3)
SCM Content
Mat Foundation
6000 psi
256
342
256
70%
Basement Walls
5000 psi
242
323
242
70%
Floors B2-1
5000 psi
512
0
341
40%
Floors 2-18
5000 psi
581
0
249
30%
Shear Walls
6000 psi
427
256
171
50%
Columns
8000 psi
503
302
201
50%

Using the Athena Impact Estimator for Buildings (Athena IE) software (www.athenasmi.org), the reference building and proposed building were defined using the proposed mixes in Table 1 and 2 respectively. Athena IE has the NRMCA benchmark mixes and the NRMCA Industry-Wide EPD mixes pre-loaded into the software. The software also permits the user to define new mixes based on the existing mixes in the library or completely new mixes if that information is available from a concrete producer.

Once all the concrete information is defined for each project, the user can then run a report that will provide the estimated GWP, along with other impacts, for each building. The reference building will represent the largest impacts and the proposed designs will represent lower impacts. The results for this example showed that the reference building has a GWP for concrete of 6.14 million kg CO2 while the proposed building has a GWP for concrete of 3.92 million kg CO2 meaning that the high volumes of fly ash and slag mixes resulted in 36% reduction in GWP as shown in Figure 2.



Figure 2. Summary of GWP for reference building and proposed building.


Keep in mind, this is example was simplified for illustration purposes. It only considered the effects of concrete during the material extraction and manufacturing stage on the environmental impacts of the building. The Athena IE software does contain environmental impact information for most materials and products used in buildings and allows input of operational energy data for conducting a more comprehensive whole building LCA.
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Thursday, June 15, 2017

Carbon Group Post 2: What About the Data?

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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. 
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Wednesday, April 19, 2017

Carbon Group Post 1: Why Climate Change Matters

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Structural Materials and Global Climate: Why Climate Change Matters

The Carbon Task Group of the Structural Engineering Institute’s Sustainability Committee has spent years studying the ways structural materials and systems affect climate change. This is the first of a planned series of blog posts on topics discussed in depth in our forthcoming technical report.

Why should we care about climate change? How bad could it be? Warmer winters will be nice, right?

We all know what’s happening. Among other things, people are burning a lot of fossil fuels—coal, oil, gas—sending unprecedented quantities of carbon dioxide and other greenhouse gases into the atmosphere. These heat-trapping gases are raising the temperature of our air and oceans, and increasing the ocean’s acidity. Polar ice is melting, contributing to sea level rise. Some regions are experiencing more intense storms and more frequent droughts. Coral reefs are dying. But so what, we can adapt, you might think. Well, maybe, but it would not be easy and the risks are great.

Even if you don’t live by the ocean or in a place where water shortages may become commonplace, there’s a lot to worry about. Climate change promises to be politically destabilizing, as millions of people find their homes inundated or food supplies threatened. They will need to resettle, increasing the flow of refugees. Political instability will lead to conflict, and yet more refugees. The Pentagon considers climate change a major risk, an “accelerant of instability” and a “threat multiplier.” We will all be affected.

And changes are coming fast. A recent study found that the United States is likely to reach an average temperature increase of at least 2°C by 2035, nearly 15 years earlier than the prediction for the globe as a whole, and that there’s at least a 50-50 chance that the northeast could reach this dubious milestone in only 10 years (Figure 1). Coastal cities such as Boston are talking about taking extreme measures to control advancing seas, such as constructing a four-mile-long, 20-foot high, barrier wall around the harbor to protect the low-lying areas of the city (Figure 2).

But the most unsettling changes are not inevitable. We still have a short amount of time to mitigate such worst-case outcomes, and structural engineers have a key role to play. We will explore what we can do in coming blog posts, so stay tuned.


Figure 1 shows the range of climate model predictions for the timing of temperature increases in the northeastern United States, the United States as a whole, and the world as a whole. The vertical axis represents the percentage of climate models that predict a given temperature will be reached by a given year.



Figure 2 shows a proposed 4-mile-long barrier wall to protect the city of Boston (Boston Globe, 17 February 2017).
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