KNIMEate Change 😋

(Some say climate change is a myth, others call it climate emergency. As I live in a country where this summer (2025) an area the size of the island of Tenerife was burned, I tend to take it seriously.

In this topic I will use KNIME to create a Data Store on climate (change) data, create some dashboards and try to draw some conclusions.

The data we will be looking at first is about carbon gas emissions.

(The next data we will be examining will be temperature evolution, starting on this page)

The data source we will be using is ClimateTrace , which offers a lot of freely available data on the Web. 

As you can see on the screenshot below, on the ClimateTrace web you can see the data organized by sector or by country.

In this PDF file you can find a detailed description of what data you will find available in the downloadable files. 

What I did first was to download the zipped file by sector to a local drive. This gave me the following result:

I then create a Workflow that loops through the files I have downloaded and extracts all files to a directory called 'Unzipped'. Below you can see the workflow and  (some of) the resulting files in the Unzipped folder:

In the following workflow we want to load the gas emission data into a staging table in our Postgres database. These data are contained in the files that have the string '_country_' in their file names.  

We accomplish this by configuring the 'List files/folders' node as seen below.

After executing this workflow, this is what I get in the cc_staging table in Postgres. The table has 188.496 rows.

There is quite a bit of redundancy in this table. This can be improved by using some of the techniques we saw in the Data Warehousing topic .

We will be applying some of these techniques on the next page.