Data-Driven and Computational Statistical Analysis of the Impact of GDP, Population, and Land Area on Renewable Energy Capacity across Countries

Abstract

This study applies a data-driven and computational statistical approach to analyze the relationships between Gross Domestic Product (GDP), population size, and land area with total renewable energy capacity across 193 countries. In the context of depleting fossil fuel reserves and increasing environmental concerns, the analysis provides quantitative insights that can guide sustainable energy planning. Data were obtained from the World Bank, the International Renewable Energy Agency (IRENA), and Worldometer, and underwent rigorous data engineering processes in Python, including extraction, cleaning, transformation, and integration. Pearson correlation analysis was employed to quantify the strength and direction of associations, while geospatial data visualization via choropleth maps illustrated spatial patterns in renewable capacity and related variables. Results show strong positive correlations between renewable energy capacity and both GDP (r = 0.76, p < 0.0001) and population size (r = 0.76, p < 0.0001), indicating that economic resources and population-driven demand substantially influence capacity expansion. Land area displayed a moderate correlation (r = 0.50, p < 0.0001), suggesting that while geographic scale can provide an advantage, it is less influential than economic or demographic factors. This study presents a novel integrated computational analysis of economic, demographic, and geographic determinants of renewable energy capacity, offering a data analytics-driven framework for policymakers, particularly in lower-capacity nations, to design targeted investment strategies and advance equitable access to renewable energy in line with global sustainability goals.