---
title: Data quality and uncertainty
description: Find out how to evaluate emission factors and activity data and use them to determine the uncertainty of your CO₂e results
---

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# Data quality and uncertainty

## Find out how to evaluate emission factors and activity data and use them to determine the uncertainty of your CO₂e results

To increase the informative value of your GHG inventory, you can **assess the data quality of your emission factors and activity data** in the Climate Hub.

Based on this, the system calculates the **uncertainty of your emission results**. This allows you to present more transparently **how reliable** your calculations are.

This feature helps you to:

- **systematically document the quality of your data**
- **make uncertainties in your CO₂ inventory transparent**
- better meet requirements from the **GHG Protocol, SBTi and reporting**
- **identify improvement potential** in your data collection

---

### **Overview: How the uncertainty assessment works**

The uncertainty of your emission calculation results from two factors:

1. **Uncertainty of emission factors**
2. **Uncertainty of activity data**

Both are **assessed separately** in the Climate Hub. You can then view the **total uncertainty of the emission factors**, the **total uncertainty of the activity data**, and the **combined total uncertainty.**

 

### **1. Uncertainty of emission factors**

For each emission factor, you can assess data quality based on **four dimensions:**

- Precision
- Geographical representativeness
- Technological representativeness
- Temporal representativeness

For each of these dimensions, the rating levels **"very good"**, **"good"**, **"fair"** and **"poor"** are available. A definition of the individual rating levels can be found in the following sub-sections 1.1 – 1.4.

You can specify the rating when creating a new activity using the "Add own activity" button. Alternatively, you can enter and edit the data quality retrospectively using the pencil icon in the relevant activity row. 

![Data quality check](https://helpdesk.verso.de/hs-fs/hubfs/Data%20quality%20check.jpg?width=670&height=259&name=Data%20quality%20check.jpg)

**Note:** If no assessment is provided, the system **automatically applies the lowest rating level**.

#### 1.1 Precision

This dimension assesses **how reliable and data-based the emission factor** is.

| **Rating level** | **Definition** |
| --- | --- |
| Very good | **Supplier-specific** emission factor |
| Good | Secondary emission factor from a **database** based on **physical quantities or consumption** |
| Fair | Secondary emission factor from a database based on the **cost** of a product or service |
| Poor | **Source or methodology** of the emission factor is **unclear** |

#### 1.2 Temporal representativeness

This dimension assesses **how up-to-date the emission factor** is compared to the reporting year.

| **Rating level** | **Definition** |
| --- | --- |
| Very good | \< 3 years deviation |
| Good | \< 6 years deviation |
| Fair | \< 10 years deviation |
| Poor | \> 3 years deviation |

#### 1.3 Geographical representativeness

This dimension assesses **how well the region of the emission factor** matches the assessed region.

| **Rating level** | **Definition** |
| --- | --- |
| Very good | Emission factor comes from the **same region**, e.g. from Germany for electricity consumption in Germany |
| Good | Emission factor comes from a **comparable region**, e.g. from Austria or France for electricity consumption in Germany |
| Fair | Emission factor comes from a **significantly different region**, e.g. from India for electricity consumption in Germany |
| Poor | Region or country not known |

#### 1.4 Technological representativeness

This dimension assesses **how well the underlying technology** corresponds to the actual process.

| **Rating level** | **Definition** |
| --- | --- |
| Very good | Emission factor was determined using the **same technology** that is actually used, e.g. an emission factor for electric cars for driving an electric car |
| Good | Emission factor was determined using a **similar technology**, e.g. an emission factor for diesel cars for driving a petrol car |
| Fair | Emission factor is based on a **generic sector value** for the entire industry, without reference to a specific technology, e.g. a transport emission factor regardless of the transport mode |
| Poor | Technology is **unknown** |

### **2\. Uncertainty of data quality**

In addition to emission factors, you can also assess the quality of your activity data. This is done via the **“Precision” column** in each activity row:

![precision\_activities](https://helpdesk.verso.de/hs-fs/hubfs/precision_activities.jpg?width=670&height=121&name=precision_activities.jpg)

A definition of the individual rating levels can be found in the table below:

| **Rating level** | **Definition** | **Precision** |
| --- | --- | --- |
| Document / Measurement | Data is based on actual measurements or documented records, e.g. electricity consumption from an annual utility bill. | -4.8 to +5.0% |
| Extrapolation | Data is extrapolated based on available information, e.g. employee commuting behavior based on a survey. | -13.0 to +15.0% |
| Spend-based | Data is based on costs instead of actual quantities, e.g. expenses for a cleaning service. | -23.1 to +30.0% |
| Estimate | Data is based on rough assumptions, e.g. estimated waste volume without measurement. | -39.4 to +65.0% |

### **3\. Output of uncertainty metrics**

Based on your assessments, the Climate Hub automatically calculates **several data quality and uncertainty metrics.** These are displayed both **per structural element** and **aggregated across all structural elements.** 

You can find the results in the report under the **"Analysis"** tab and the **"Data Quality & Uncertainty"** sub-tab:

![Data quality & uncertainty](https://helpdesk.verso.de/hs-fs/hubfs/Data%20quality%20%26%20uncertainty.jpg?width=670&height=394&name=Data%20quality%20%26%20uncertainty.jpg)

#### 3.1 Data quality metrics for emission factors

This section shows the data quality of your emission factors by category for the four dimensions:

**Precision:**  
Describes how reliable the emission factors used generally are.

**Temporal data quality:**  
Shows how well the time frame of the emission factors aligns with your reporting period.

**Geographical data quality:**  
Assesses how well the geographical origin of the emission factors matches your actual region.

**Technological data quality:**  
Indicates how well the underlying technology of the emission factors corresponds to your actual process.

The following key figures are calculated for each dimension:

**Multiplicative uncertainty factor:**  
Shows how much your calculated emissions can deviate upwards or downwards.

Example:   
An uncertainty factor of 1.2 means that your actual emissions can be up to 20% higher or lower.

**Upper and lower deviation of the CO₂e emissions:**  
Shows the percentage deviation of your emissions, either above or below the target.

**Confidence interval:**  
Indicates the possible deviation in your emissions in absolute terms.

#### 3.2 Uncertainty metrics

This section shows

- the aggregated data quality of your emission factors across all dimensions,
- the total uncertainty of the activity data, and
- the combined total uncertainty derived from emission factors and activity data.

**Total uncertainty of emission factors:**  
Shows the combined uncertainty of all emission factors, considering all four quality dimensions.

**Total uncertainty of activity data:**  
Shows the uncertainty of the activity data used. A low uncertainty indicates that your data is mainly based on reliable primary data. A high uncertainty indicates that estimates were used.

**Total uncertainty of emission factors and activity data:**  
Shows the overall uncertainty of your emission calculation by combining both emission factor and activity data uncertainties.

Here too, the following metrics are displayed for each category:

- Multiplicative uncertainty factor
- Upper and lower limits
- Confidence interval.

**Attention:**

- For **Scope 2 categories**, you must specify the **rating dimensions separately for the market-based and location-based approaches**. The uncertainty metrics are then reported separately for both approaches.

- Currently, **only the direct emission factor** is included in the uncertainty assessment. The **upstream emission factor is not yet considered**.

#### 3.3 Breakdown of key figures by structural element and in aggregate

All key figures are displayed both **by structural element** and **aggregated across all structural elements**.

**View key figures by structural element:**  
To view the key figures by structural element, select the desired element from the "Structural element" drop-down menu. The key figures will then be displayed for the selected structural element.

![Data\_quality\_per\_location](https://helpdesk.verso.de/hs-fs/hubfs/Data_quality_per_location.jpg?width=670&height=383&name=Data_quality_per_location.jpg)

**Display aggregated metrics:**  
To view aggregated data across all structural elements, navigate to the top-level hierarchy. Then click the “Include sub-structure” button. 

![Data quality & uncertainty-1](https://helpdesk.verso.de/hs-fs/hubfs/Data%20quality%20%26%20uncertainty-1.jpg?width=670&height=394&name=Data%20quality%20%26%20uncertainty-1.jpg)

 

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