## Methodology

This section outlines the rigorous methodology employed for data collection, processing, and normalization to ensure the robustness and comparability of the Sunstone Energy Transition Score (SETS).

**1.1 Data Collection**

Data for the SETS is sourced from a diverse set of reputable international organizations and national agencies, as detailed in Table . This multi-source approach minimizes reliance on single data providers and enhances data integrity. Key considerations during data collection include:

* **Global Coverage:** Prioritizing datasets with extensive country coverage to ensure the SETS's global representativeness.  
* **Time Series Consistency:** Selecting data series that allow for consistent tracking of indicators over time, facilitating trend analysis.  
* **Definition Harmonization:** Carefully reviewing and, where necessary, harmonizing indicator definitions across different sources to ensure comparability.


**Table 1: Data Sources by Sector**

| Source | Datasets |
| :---- | :---- |
| Emissions Database for Global Atmospheric Research (EDGAR) | Greenhouse Gas Emissions |
| Energy Institute | Fossoil Fuel Production and Consumption |
| International Renewable Energy Agency (IRENA) | Renewable Energy Production and Installation  |
| U.S. Energy Information Administration (EIA) | Fossoil Fuel Production and Consumption,  Electricity Statistics, Greenhouse Gas Emissions |
| International Energy Agency (IEA) | Fossoil Fuel Production and Consumption, Electricity Statistics,  Electric Vehicles |
| World Health Organization (WHO) | Access to Clean Cooking |
| World Bank | Population,  Gross Domestic Product (GDP), Access to Electricity |
| UN Trade and Development | International Trade, Maritime Transport |

**1.2 Data Validation and Pre-processing**

Raw data often contains inconsistencies, missing values, or outliers that can skew analytical results. A systematic pre-processing and cleaning protocol is applied:

* **Handling Missing Values:** For individual missing data points, estimations are sought by finding the closest values or by calculating an average of adjacent years. If data is extensively missing, a determination is made regarding the meaningful inclusion of the indicator.  
* **Outlier Detection and Treatment:** Statistical methods (e.g., Z-score) are employed to identify outliers. Outliers are then either winsorized (capped at a certain percentile) or treated through imputation, depending on their impact and underlying cause.  
* **Unit Conversion:** All data points are converted to consistent units to ensure accurate aggregation and comparison.

**1.3 Normalization**

To aggregate diverse indicators with different scales and units into a single composite index, each indicator is normalized. Min-Max normalization is applied, transforming all indicator values to a scale between 0 and 100\. This ensures that each indicator contributes proportionally to the overall SETS score, regardless of its original magnitude.

This ensures that for all indicators, a higher normalized score consistently indicates better performance.

**1.4 Weighting and Aggregation**

As detailed in Section 2.3 and Table 2, the SETS is calculated as a weighted sum of the normalized scores of its constituent indicators. The assigned weights reflect the perceived relative importance of each component in the overall energy transition. The aggregation formula is:

SETS \= Σ (Normalized Indicator Score \* Indicator Weight)

Where the sum is taken over all selected indicators, and the weights sum up to 100%.

**1.5 Trend Score Calculation**

To capture the dynamism of a country's energy transition, Trend Scores are calculated for key indicators (GHG Emissions per Capita, Low Carbon Electricity Capacity per Capita, Fossil Fuel Production per Capita). A linear regression model is applied to the past decade of data for each indicator to determine its average annual rate of change. This rate of change is then normalized and incorporated into the overall SETS calculation, providing insight into the momentum of a country's transition efforts.

**1.6 Data Validation and Review**

A continuous data validation process is implemented, involving:

* **Cross-referencing:** Key data points are cross-referenced with alternative reputable sources to confirm accuracy.  
* **Expert Review:** The selection of indicators, data sources, and methodological approach undergoes periodic review by subject matter experts.  
* **Transparency:** The methodology and underlying data are made publicly available where possible to foster transparency and allow for independent verification.  
  
## Why we chose these indicators and weights

The indicators and their corresponding weights, detailed in Table 2 of section 2.3 of the [SETS report](https://sunstone.institute/api/data/report/energy-transition-report/download), are grouped into four weighted categories:

* Greenhouse Gas Emission (30%)  
* Low Carbon Electricity Capacity (30%)  
* Fossil Fuel Production (20%)  
* Infrastructure & Innovation (20%)

### Greenhouse Gas Emission (30%)

The paramount objective in combating climate change is to reduce greenhouse gas emissions to a sustainable level that can be naturally absorbed. Therefore, **greenhouse gas emissions per capita** are deemed the most critical and equitable metric for cross-country comparisons.

Given its significance, this category is assigned a **30% weight**. This weight is split: **20%** is allocated to the *current per-capita emissions level, and the remaining **10%*** to the *trend over the last 10 years*. This trend component reflects a country's recent commitment and efforts toward reducing greenhouse gas emissions.

### Low Carbon Electricity Capacity (30%)

Energy is the cornerstone of modern society, with fossil fuels having historically played a vital role. However, the time has come to transition to low carbon energy sources, specifically renewable and nuclear energy. Given that our lives fundamentally rely on energy, this category is weighted at 30%.

**Low Carbon Electricity Capacity per Capita vs. Low Carbon Energy Share**

While the percentage share of low carbon energy in total consumption is a useful metric for indicating a country's reliance on low carbon sources, it does not adequately reflect a country's level of advancement or its citizens' standard of living. A progressive nation must not only provide energy to its people but also ensure that energy is low carbon. Therefore, we chose to use **Low Carbon Electricity Capacity per Capita (MWatt/capita)** as a better indicator. This metric directly measures the capacity of low carbon electricity a country has successfully developed for its population, offering a clearer picture of its actual progress toward low carbon energy adoption.

Of the total 30% weight allocated to the energy category, 20% is assigned to the current capacity level, and 10% to the trend observed over the past 10 years.

### Fossil Fuel Production (20%)

**Rationale for Selecting Fossil Fuel Production over Consumption**

Our choice to measure **Fossil Fuel Production per Capita** rather than consumption is driven by two key reasons: to avoid double-counting and to ensure true accountability. Since Fossil Fuel Consumption is directly correlated with greenhouse gas emissions, including it would essentially double-count the emissions impact already captured by other indicators.

The production metric acts as a crucial "supply-side" control, shifting the focus from national burning habits to national extraction activity. This is a powerful tool for exposing hypocrisy: it holds countries accountable for their carbon exports, revealing those that maintain a "green" domestic image while profiting from massive fossil fuel sales abroad.

Furthermore, this indicator quantifies economic risk by showing how deeply an individual's prosperity is tied to potential "stranded assets" in the event of a collapse in global demand. While most metrics focus on consumption, this approach targets the problem's source by calculating the volume of CO2 pulled from the ground per person. This determines whether a country is genuinely aiding the global transition or merely "cleaning its own house" while fueling a worldwide crisis.

**Indicator Weighting**

We allocated a **20% weight** to this category, deeming it equally important to the Infrastructure & Innovation category, which also received 20%. This 20% is split to reflect both current status and recent trajectory: **13.3%** for the current production level and **6.7%** for the trend observed over the last 10 years.

### Infrastructure & Innovation (20%)

The "Energy Access and Efficiency" category comprises four indicators: Energy Efficiency, Electricity Distribution Efficiency, Access to Electricity, and Access to Clean Cooking. The first two indicators assess the performance of the infrastructure and innovation within the energy system. The latter two are crucial for capturing the socio-economic dimension of the energy transition. Each of these four indicators has been assigned a 5% weight, making the total weight for this category 20%.

It's important to note that other significant innovations, such as electric vehicle adoption, smart grid development, and large-scale energy storage utilities, are not currently included in the score calculation. This omission is due to the lack of globally consistent and sufficient data. We plan to incorporate these indicators once adequate data becomes available.

## Limitations and Challenges
While the methodology is built on reputable data and clear rationale, several inherent constraints remain:

* **Data Dependencies:** Calculations are based on the best available data from international organizations; however, results are inherently limited by the consistency and accuracy of these external datasets.  
* **Subjectivity in Weighting:** No weighting system is universally definitive. Alternative priorities among different stakeholders could lead to different scoring outcomes.  
* **Exclusion of Emerging Tech:** The model focuses on realized, quantifiable numbers. High-potential but data-scarce innovations—such as fusion and carbon capture—are currently excluded from the calculations.