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Author's profile photo Muralikrishnan Ethiraj

Understanding the CO2 emission using SAP Visual Intelligence

Hello Everyone,

In this post, I would like to brief the usage of SAP Visual Intelligence to understand the CO2 emissions by drilling down to the data

The data can be downloaded from

As we have an option in Visual Intelligence,to explore the data directly from Excel,just browsed for the source file and selected the required fields to explore and finally acquired the data.

I wanted to find the Country which contributes for the high CO2 emission.Since Visual Intelligence has a good geographical maps,i thought of exploring the data using it.

Create a Geographical Hierarchy on Country Name


Add this under Dimensions with,the Population values of year 2010 as Measure


China has a higher value and contributing for the huge population growth

Just to identify the Top 15 Countries who contributed for the global population growth,filter the top 15 values


As China is in No.1,it’s possible to know what kind of CO2 emissions this Country makes with the help of Visual Intelligence by applying further filtering and Treillis.

Filter the Country Name by China and move the CO2 emissions type(Indicator Name) under Treillis.This shows the different types of emissions during various years in China as shown below


Out of different CO2 emissions,let’s focus on “Emissions by residential and commercial buildings”.Filter the third chart to focus more on that


This chart shows that the “Emissions by residential and commercial buildings” are high in the year of “1990”

To find why its high in this particular year,we can add additional Indicator “Urban Population Growth”


This clearly indicates that when there is a high Urbal Population Growth,the amount of CO2 emission from residential and commercial buildings are more.

Thus SAP Visual Intelligence helps the casual business users to dive into the data (though he doesnt have much technical knowledge about the data modeling) and come up with the conclusion with a very good visualisation capability.

Rgds, Murali

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      Author's profile photo Henry Banks
      Henry Banks

      Cool insights, thanks. Another chart suggestion:

      • it would be neat to have both Population and C02 on 2 Y-Axis, with Time on X-axis, to see the year-over-year correlation (presumably linear / exponential growth ).  
      • You could even use the Predictive functions in VI for Forecast future trends (assuming you have 'smart dimension' Time hierarchy available.). 
      • And you could split using Trellis for the top 3 countries (China India US) to compare the relative acceleration of polution per territory.