There are 15 Sustainable Development Goals (SDG) according to Eurostat. Some of the goals among these 15 are clean energy and ways to reduce green house gasses into the atmosphere. Between 2000 and 2016, the number of people with electricity increased from 78% to 87%. As population continues to grow, so will the demand for cheap energy. Investing in alternating source of power other than fossil fuels is vital to the environment and reducing the amount of carbon and other green house gasses in the atmosphere.
What it does
The Goal 13 is the climate action has an action plan to reduce the amount Greenhouse gas emissions into the atmosphere. SDG_13_20 focuses on Energy related Green House Gas Emissions pertaining to Goal 13 that is Climate Action. The data from "Eurostat - Greenhouse Gas Emissions Intensity of Energy Consumption" delivered by CRUX focuses on SDG_13_20 which focuses on affordable and clean energy. The data shows an indicator value which represents the energy related Green House Gas emissions and Gross inland consumption of energy.
How I built it
Subscribed to "Eurostat - Greenhouse Gas Emissions Intensity of Energy Consumption" delivered by CRUX using Amazon AWS Data Exchange. New datasets are added once a year for this product, so I took the latest revision of data set available in AWS Data Exchange and stored it in AWS S3. I extracted the data from .CSV and .GeoJSON datasets in S3 and created the tables using Extract, Transform, Load function and crawlers in AWS Glue. I created table "greenhouse_eucx05564", that can be used in AWS Athena to create queries, group queries based on a condition and to filter data. For data visualization, interpretation and calculations for predictions, I used AWS QuickSight. The input data for generating visualizations in QuickSight is from Athena.
Challenges I ran into
There were number of decision factors to consider apart from challenges. Firstly, the decision to use AWS CloudfFormation is skipped as the Eurostat data is updated yearly and not weekly, daily or monthly . Also, instead of using QuickSight inbuilt forecasting or insights to predict the future data, I used "parameter" option in QuickSight to create a new value which is a calculation based on data from the past .
Accomplishments that I'm proud of
Learnt new topics in AWS and how to use the data visualization tool - QuickSight. Having subscribed to "Eurostat - Greenhouse Gas Emissions Intensity of Energy Consumption" by Crux, learnt a lot about using, interpreting data in QuickSight and the power of data and tools like AWS-QuickSight to make informed decisions and actions pertaining to the environment.
What I learned
Apart from the technical stuff, also learnt about the various Sustainable Development Goals (SDG) that can have a positive impact from the individual's personal well-being to Global - Environmental. The use and collection of environmental data and the power of tools like QuickSight to represent and be used for predictive and forecasting purposes, to make more informed decisions, and take actions that have less harmful impact on the environment.
What's next for Carbon print in the Environment - EU
If the same data exist for US then combine the data and compare the Green House Gas emissions pertaining to US with other countries. Include other developing countries in the list and visualize and interpret data.