obayori-emmanuel/global-energy-transition-tracker

SQL and Excel analysis of global renewable energy growth, fossil fuel dependency, and electricity demand (2014–2024), with a focused look at Nigeria's energy profile. 🌍 Global Energy Transition Tracker (2014–2024) > A SQL and Excel analysis of renewable energy growth, fossil fuel dependency, and electricity demand across countries — with a focused look at Nigeria's energy profile. --- 📌 The Question Which countries are moving fastest toward renewable energy? Which remain most fossil-fuel dependent? How is electricity demand growing globally? And specifically — what does Nigeria's energy consumption pattern reveal about its reliance on non-electric energy sources? --- 🗂️ Project Files File Description `Global\_Energy\_Transition\_Tracker.xlsx` Full Excel workbook — Dashboard tab + 4 data sheets `energy\_transition\_tracker\_queries.sql` All PostgreSQL queries used in the project `dashboard-screenshot.png` Dashboard visual (2×2 chart layout) `README.md` This file --- 🔧 Tools Tool Purpose PostgreSQL Data loading, cleaning, and analytical querying Microsoft Excel PivotTables, PivotCharts, and dashboard assembly --- 📊 Data Source Our World in Data — Energy Dataset Compiled from Ember and the Energy Institute's Statistical Review of World Energy. Coverage: ~240 countries and regional aggregates, 1900–2025. --- 🔄 Method Loaded the OWID energy dataset into PostgreSQL, trimming to 10 relevant columns from the original ~122. Filtered out regional aggregates (e.g. "ASEAN", "Europe", "World") using `iso\_code`, isolating actual countries only. Wrote analytical queries to rank countries by: Renewable energy growth (2014–2024) Fossil fuel dependency (2024) Electricity demand growth per capita (2014–2024) Built a dedicated Nigeria trend query comparing total energy consumption against electricity demand, plotted on a dual-axis line chart. Exported query results to Excel and assembled a 2×2 PivotChart dashboard. --- 🔍 A Data Integrity Issue — and How It Was Caught Midway through the analysis, a sense-check on Germany and the US revealed implausible values — `energy\_per\_capita` was showing up lower than `electricity\_demand\_per\_capita`, whic …