In this project, I visualized the distribution of carbon dioxide emissions per capita in Kazakhstan for the year 2023. The visualization was created using the R programming language and libraries such as tidyverse, geodata, terra, exactextractr, sf, and classInt.
Methods and Tools
The following tools and libraries were used for creating this visualization:
- tidyverse: A collection of packages for data manipulation and visualization.
- geodata: A package for accessing and working with geographic data.
- terra: A library for spatial data analysis.
- exactextractr: A package for fast extraction of summary statistics from rasters.
- sf: A package for simple features, used for handling vector data.
- classInt: A package for choosing univariate class intervals.
Visualization Description
The map graphically represents the carbon dioxide emissions per capita across different regions of Kazakhstan. The darker the area, the higher the CO2 emissions.
Conclusions
Analyzing the map allows us to draw several key conclusions:
1. Aksu District in Pavlodar Region: This district is considered the most polluted in terms of CO2 emissions.
2. Ekibastuz District in Pavlodar Region: The neighboring district ranks second in CO2 emissions.
3. Birzhan Sal District in Akmola Region: This district follows in terms of high CO2 emissions.
4. Moiynkum District in Zhambyl Region: This district also stands out with its darker color indicating higher emissions compared to neighboring districts.
Resolution
According to UNDP Kazakhstan, the country ranks among the top 10 in the world for CO2 emission intensity. The majority of CO2 emissions come from the industrial sector. Notably, 35% of the country's electricity consumption is attributed to electric motors, particularly transformers. This work underscores the importance of addressing industrial emissions and exploring sustainable energy solutions.
Code
The entire project code is available on GitHub