Key Factors of Global Energy Consumption - Data Analysis with Python

Buildings account for a large portion of global energy consumption. The future development of global energy consumption depends on three key factors: population increase, economic development and energy intensity (1). In this post, i am going to visualise publicly available raw data related to these key factors with diagrams made in python with matplotlib.

 

Global population increase until 2015

World population data was retrieved from the United Nations world population prospects website. The available data covers the years 1950 to 2100. The first chart is a stacked area chart showing the population of the all world regions until 2015.

 

To show the differences in population development of the separate regions more clearly, I plotted the same data as a multiple chart. 

 

Global population increase, future development

The figures from 1950 to 2015 are estimates, the figures after 2015 are projections based on the 'medium fertility variant'. The red dashed line shows the year 2015 representing the separation between estimates and projections.

This multiple chart shows the population separately for each region. Again, the raw data is the same like in the graph above.

 

Global economic growth

The following diagram shows the average annual GDP growth per capita and by country for the last five years (2011 - 2016). The raw data was retrieved from the World Bank Data Bank. Countries with dark purple colours have the highest GDP growth.

This is a screen shot of the original svg-file that can be downloaded here. The svg-file is interactive and shows the GDP growth figures of each separate country.

Global primary energy consumption

The most comprehensive data on primary energy consumption by regions I could find was the BP Statistical Review of World Energy 2016.  It covers the years 1965 to 2015. Unfortunately, the classification by regions is slightly different from the previous graphs. Note that this data includes only commercially-traded fuels (coal, oil, gas), nuclear and modern renewables used in electricity production. As such, it does not include traditional biomass sources. Unfortunately, the classification into regions doesn't correspond exactly to the one used for the data on population growth.

Next, you can see the same data as a multiple chart showing the individual regions.

Data Visualisation

All raw data was retrieved in the form of csv-files from the respective websites. The interactive world map showing the GDP per capita growth 2011 - 2016 was made in Python with the Pygal module. All other graphs were made in Python with the modules Matplotlib, Seaborn and Pandas.

 

References

(1) cf. Daniels, Klaus; Hammann, Ralph E.: Energy Design for Tomorrow, S. 104