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🌍 Renewable Energy Effectiveness — A Data-Driven Analysis (2000–2019)

Do renewable energies work the same way everywhere?
This project investigates 20 years of global energy data across 200+ countries to answer that question — and find out where renewable investments actually deliver CO₂ reductions.


📌 Project Overview

This is an end-to-end data analysis project built in Power BI, examining the relationship between renewable energy adoption, energy consumption, and CO₂ emissions worldwide from 2000 to 2019.

The analysis is structured as a 3-tab interactive dashboard that progressively builds an investment thesis:

Tab Title Focus
1 The Renewable Paradox: Global Trends 2000–2020 Macro-level global trends
2 Renewables vs Emissions Country-level champions & failures
3 Investment Analysis Segmentation model & target identification

🔍 Key Findings

1. The Global Paradox

Despite widespread rhetoric around the energy transition, the global average tells a sobering story:

  • CO₂ emissions increased by +48.05% between 2000 and 2019
  • Renewable energy share declined by −3.19 percentage points globally
  • Energy consumption per capita grew by +6.64%

Renewables are not keeping pace with rising global demand.

2. Even Champions Have Mixed Results

Among the top 10 countries by renewable energy growth, outcomes are split:

  • Denmark: +26.79 pp renewables → −43.54% CO₂ (success)
  • Iceland: +20.41 pp renewables → −26.46% CO₂ (success)
  • Bosnia: +17.67 pp renewables → +50.93% CO₂ (failure)
  • Uruguay: +22.03 pp renewables → +18.86% CO₂ (failure)

Success rate: only 57% — adding renewables is no guarantee of emission reductions.

3. Consumption Context Is Everything

A 2×2 segmentation model reveals why:

Profile Consumption Renewables Countries Strategy
Q1 – Intensive High High 4 (2.3%) Selective — large projects only
Q2 – Optimal Low High 66 (37.5%) Maintain — benchmark model
Q3 – High Potential Low Low 52 (29.5%) Priority — best ROI
Q4 – Avoid High Low 54 (30.7%) Avoid — structural barriers

Only 4 countries in the world achieve high consumption + high renewables — proving this combination is extraordinarily rare.

4. Investment Recommendation: Jamaica 🇯🇲

Among Q3 countries with proven CO₂ reductions:

Country CO₂ Change
Jamaica −16.60%
Cuba −8.61%
North Macedonia −5.25%

Jamaica leads as the top Q3 performer, validating the thesis that low-consumption contexts allow renewable investments to directly displace fossil fuels.


📊 Dashboard Structure

Tab 1 — Global Trends

  • Line Chart: Total global CO₂ emissions (2000–2019) — fact_energy_metrics.Total_CO2
  • Line Chart: Average energy consumption per capita — fact_energy_metrics.Total_Consumption
  • Line Chart: Global average renewable energy share — fact_energy_metrics.Avg_Renewables_Pct
  • KPI Cards: CO₂ Change %, Renewables Change (pp), Consumption Change %

Tab 2 — Country Analysis

  • Scatter Plot: Renewable share vs. Carbon Intensity per country, sized by consumption — shows clustering and outliers
  • Table: Top 10 countries by renewable growth with CO₂ outcomes
  • Bar Chart: CO₂ variation for top renewable performers (green = reduction, blue = increase)

Tab 3 — Investment Analysis

  • Scatter Plot: 2×2 country segmentation by avg. consumption × avg. renewable share, colored by profile
  • Donut Chart: Country distribution across the 4 profiles
  • Tables: Profile investment strategies + top Q3 target countries

🗂️ Data Model

The report uses a star schema with the following tables:

dim_year          — date dimension (year)
dim_country       — country dimension (name, profile classification)
Country_Profiles  — profile lookup (Q1–Q4 labels, investment strategies)
fact_energy_metrics — central fact table

Key measures in fact_energy_metrics:

Measure Description
Total_CO2 Total CO₂ emissions
Total_Consumption Energy consumption
Avg_Renewables_Pct Average renewable energy share
CO2_Change_Pct % change in CO₂ over period
Renewables_Change_Pts Change in renewable share (percentage points)
Consumption_Change_Pct % change in consumption
Carbon_Intensity CO₂ per unit of economic output
Avg_Consumption Average per capita consumption
Renewables_Growth_2000_2019 Net renewable growth over full period
Country_Count_By_Profile Count of countries per segment

🛠️ Tools & Technologies

Tool Usage
Power BI Desktop Dashboard development, DAX measures, data modeling
Power Query (M) Data cleaning and transformation
DAX Custom KPI measures, segmentation logic, YoY calculations

📁 Repository Structure

renewable-energy-analysis/
│
├── README.md                              ← You are here
├── dataviz_test.pbix                      ← Power BI report file
│
├── data/
│   ├── raw/                               ← Original source datasets (CSV/Excel)
│   └── processed/                         ← Cleaned data used in the model
│
├── docs/          
│   └── screenshots/                       ← Dashboard screenshots (PNG)
│       ├── tab1_global_trends.png
│       ├── tab2_country_analysis.png
│       └── tab3_investment_analysis.png
│
└── notes/
    └── methodology.md                     ← Data cleaning decisions & assumptions

🚀 Getting Started

Prerequisites

  • Power BI Desktop (free) — Windows only
    macOS/Linux users: use Power BI service via browser, or a Windows VM

Running the Report

  1. Clone or download this repository
  2. Open dataviz.pbix in Power BI Desktop
  3. The data is embedded in the model — no external connection needed
  4. Navigate between the 3 tabs using the NEXT buttons or the Pages panel

📐 Methodology

  • Time period: 2000–2019 (pre-COVID baseline, 20 full years)
  • Coverage: 200+ countries
  • Segmentation: Countries classified into Q1–Q4 profiles using median splits on average per-capita energy consumption and average renewable energy share
  • CO₂ metric: Absolute total emissions and % change over the full period
  • Carbon Intensity: CO₂ relative to economic output (captures efficiency, not just volume)
  • Top 10 renewable growers: Ranked by net percentage-point gain in renewable share from 2000 to 2019

💡 Insights & Limitations

What this analysis shows well:

  • The gap between renewable investment and actual emission reductions
  • Why consumption context is a better predictor of success than renewable share alone
  • A replicable segmentation framework for prioritizing investment targets

Limitations to keep in mind:

  • Data ends at 2019 — the 2020s energy transition (IRA, REPowerEU, etc.) is not reflected
  • Country-level averages mask regional and sub-national variation
  • Carbon intensity uses economic output as denominator, which can shift with GDP changes independently of energy policy
  • "Renewable energy share" includes traditional biomass, which may not represent modern clean energy in all countries

📄 Data Sources

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