This map visualizes K-Means clustering of 227 countries based on four key socioeconomic indicators: GDP per capita, renewable energy consumption, greenhouse gas emissions per capita, and inflation rates. The clustering reveals distinct development patterns:
Cluster 0 (Orange, 62 countries): Developing economies primarily in Sub-Saharan Africa and parts of Asia. These countries show moderate GDP levels with transitional energy systems and relatively stable inflation.
Cluster 1 (Cyan, 126 countries): The largest cluster, representing lower-income countries across Africa, South Asia, and parts of Latin America. These nations often have higher reliance on renewable energy (primarily traditional biomass and hydropower) due to limited fossil fuel infrastructure rather than deliberate climate policy.
Cluster 2 (Red, 3 countries): High-emissions outliers with exceptionally high per-capita greenhouse gas emissions relative to their economic profiles. These represent unique cases with extreme energy consumption patterns.
Cluster 3 (Mint, 36 countries): High-income developed nations including the USA, Canada, Western Europe, Australia, and Japan. These countries have high GDP per capita, advanced infrastructure, and relatively stable economic indicators, though their environmental profiles vary significantly.
The geographic distribution shows clear patterns: most of Africa and South Asia cluster together (cyan), Latin America shows mixed development stages (orange and cyan), while North America, Europe, and Oceania form the high-income cluster (mint). This clustering successfully captures the global economic divide while revealing that environmental sustainability does not automatically correlate with wealth—policy choices matter more than income levels.
Across the different scatterplots, a consistent structural pattern seems to apply: countries cluster into three broad groups - low-GDP with high social/environmental stress, middle-GDP with transitional characteristics, and high-GDP with consistently low poverty but highly variable environmental impact.
In the GDP vs. Poverty plot, the clustering is especially prominent. Low-income countries are tightly grouped at the upper left, with very high poverty headcount ratios regardless of region. As GDP rises, poverty rates fall sharply, forming a middle band of transitioning economies. Eventually, high-income countries cluster near the bottom, showing minimal poverty across the board. This reflects a well-established empirical pattern: rising national income tends to reduce extreme poverty.
When examining GDP vs. Renewable Energy Use, the same three-group structure reappears, but with a crucial difference: the direction of the relationship is weaker and far more dispersed. Low-GDP countries tend to report higher renewable energy use - primarily because they rely on traditional biomass and have limited access to fossil-fuel infrastructure. Middle-income economies form a scattered mid-range cluster, while high-GDP countries spread widely across the lower half of renewable energy consumption. This dispersion indicates that economic development does not reliably predict low-carbon energy adoption; instead, energy portfolios depend on policy choices, geography, historical investment patterns, and resource endowments.
Taken together, these graphs reveal that economic success strongly correlates with improvements in social indicators like poverty reduction or literacy, but not necessarily with environmental sustainability. High GDP per capita does not guarantee lower greenhouse gas emissions or higher renewable energy adoption; in fact, many wealthy countries exhibit high emissions and low renewable shares. This suggests that environmental outcomes are not automatic byproducts of development, but rather outcomes that must be deliberately engineered through policy and technological investment.